from IPython.display import display
import arcgis
from arcgis.gis import GIS
import pandas as pd
pd.set_option('display.max_columns', 500)
from arcgis.features import FeatureLayer
from arcgis.mapping import WebMap
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import time

gis = GIS("https://datascienceqa.esri.com/portal", "portaladmin", "esri.agp", verify_cert=False)
# gis = GIS("https://datascienceqa.esri.com/portal", "portaladmin", "esri.agp")

# Layers Used
# Provider data layer
# provider_layer = FeatureLayer("https://datascienceqa.esri.com/server/rest/services/Hosted/provider_clean_data_geocoded_6_19/FeatureServer/0")
provider_layer = FeatureLayer("https://datascienceqa.esri.com/server/rest/services/Hosted/provider_data_geocoded_7_23/FeatureServer/0")

# Population Density
from arcgis.mapping import MapImageLayer
popdensity = MapImageLayer("https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer")

# Median Income
medIncome = MapImageLayer("https://datascienceqa.esri.com/portal/sharing/servers/3e5f8ebe5a114a61b7f350e7a1203761/rest/services/USA_Demographics_and_Boundaries_2018/MapServer")

# Median Age
medAge = MapImageLayer("https://datascienceqa.esri.com/portal/sharing/servers/e2558ac0c5e04235ad7820773e89d110/rest/services/USA_Demographics_and_Boundaries_2018/MapServer")

# Health Insurance Coverage
tx_insurance_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer")
tx_insurance_county = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer/1")
tx_insurance_tract = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer/2")

# Fertility layers for State, County and Tract (Percent of women 15 to 50 who had a birth in the past 12 months)
fertility_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/0")
fertility_county = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/1")
fertility_tract = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/2")

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-4-1123c4387856> in <module>
      6 # Population Density
      7 from arcgis.mapping import MapImageLayer
----> 8 popdensity = MapImageLayer("https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer")
      9 
     10 # Median Income

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\mapping\_types.py in __init__(self, url, gis)
   1962         super(MapImageLayer, self).__init__(url, gis)
   1963 
-> 1964         self._populate_layers()
   1965         self._admin = None
   1966         try:

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\mapping\_types.py in _populate_layers(self)
   1979         tables = []
   1980 
-> 1981         for lyr in self.properties.layers:
   1982             if 'subLayerIds' in lyr and lyr.subLayerIds is not None: # Group Layer
   1983                 lyr = Layer(self.url + '/' + str(lyr.id), self._gis)

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\gis\__init__.py in properties(self)
   9534         else:
   9535             self._hydrate()
-> 9536             return self._lazy_properties
   9537 
   9538     @properties.setter

AttributeError: 'MapImageLayer' object has no attribute '_lazy_properties'

Get Provider Data¶

Provider data was geocoded using the GeoAnalytics server. Let's get the geocoded provider data feature layer for exploration.

# search_result = gis.content.search('title: npidata_Geocoded')
search_result = gis.content.search('title: provider_data_geocoded_7_30', 'Feature Layer')

provider_data_item = search_result[0]
provider_data_item

provider_data_item.layers

[<FeatureLayer url:"https://datascienceqa.esri.com/server/rest/services/Hosted/provider_data_geocoded_7_30/FeatureServer/0">]

provider_data_layer = provider_data_item.layers[0]
provider_data_layer

<FeatureLayer url:"https://datascienceqa.esri.com/server/rest/services/Hosted/provider_data_geocoded_7_30/FeatureServer/0">

# Look at the fields and their data types
for f in provider_data_layer.properties.fields:
    print(f['name'],'      ',f['type'])

objectid        esriFieldTypeOID
user_npi        esriFieldTypeDouble
user_entity_type        esriFieldTypeString
user_organization_name        esriFieldTypeString
user_address        esriFieldTypeString
user_address2        esriFieldTypeString
user_city        esriFieldTypeString
user_region        esriFieldTypeString
user_postal        esriFieldTypeDouble
user_country        esriFieldTypeString
user_provider_gender        esriFieldTypeString
user_taxonomy_code_1        esriFieldTypeString
user_taxonomy_code_2        esriFieldTypeString
user_taxonomy_code_3        esriFieldTypeString
user_taxonomy_code_4        esriFieldTypeString
user_taxonomy_code_5        esriFieldTypeString
user_taxonomy_code_6        esriFieldTypeString
user_taxonomy_code_7        esriFieldTypeString
user_taxonomy_code_8        esriFieldTypeString
user_taxonomy_code_9        esriFieldTypeString
user_taxonomy_code_10        esriFieldTypeString
user_taxonomy_code_11        esriFieldTypeString
user_taxonomy_code_12        esriFieldTypeString
user_taxonomy_code_13        esriFieldTypeString
user_taxonomy_code_14        esriFieldTypeString
user_taxonomy_code_15        esriFieldTypeString
user_taxonomy_group_1        esriFieldTypeString
user_full_address        esriFieldTypeString
in_address        esriFieldTypeString
in_address2        esriFieldTypeString
in_city        esriFieldTypeString
in_postal        esriFieldTypeDouble
in_region        esriFieldTypeString
status        esriFieldTypeString
score        esriFieldTypeDouble
match_addr        esriFieldTypeString
addr_type        esriFieldTypeString
loc_name        esriFieldTypeString
longlabel        esriFieldTypeString
shortlabel        esriFieldTypeString
type        esriFieldTypeString
placename        esriFieldTypeString
place_addr        esriFieldTypeString
phone        esriFieldTypeString
url        esriFieldTypeString
rank        esriFieldTypeDouble
buildingname        esriFieldTypeString
addressnumber        esriFieldTypeString
addnumfrom        esriFieldTypeString
addnumto        esriFieldTypeString
addressrange        esriFieldTypeString
side        esriFieldTypeString
stpredir        esriFieldTypeString
stpretype        esriFieldTypeString
stname        esriFieldTypeString
sttype        esriFieldTypeString
stdir        esriFieldTypeString
bldgtype        esriFieldTypeString
bldgname        esriFieldTypeString
leveltype        esriFieldTypeString
levelname        esriFieldTypeString
unittype        esriFieldTypeString
unitname        esriFieldTypeString
subaddress        esriFieldTypeString
staddr        esriFieldTypeString
block        esriFieldTypeString
sector        esriFieldTypeString
neighborhood        esriFieldTypeString
district        esriFieldTypeString
city        esriFieldTypeString
metroarea        esriFieldTypeString
subregion        esriFieldTypeString
region        esriFieldTypeString
regionabbr        esriFieldTypeString
territory        esriFieldTypeString
zone        esriFieldTypeString
postal        esriFieldTypeString
postalext        esriFieldTypeString
country        esriFieldTypeString
langcode        esriFieldTypeString
distance        esriFieldTypeDouble
x        esriFieldTypeDouble
y        esriFieldTypeDouble
displayx        esriFieldTypeDouble
displayy        esriFieldTypeDouble
x_min        esriFieldTypeDouble
x_max        esriFieldTypeDouble
y_min        esriFieldTypeDouble
y_max        esriFieldTypeDouble
extrainfo        esriFieldTypeString

# Check number of records in `provider_data_layer`
provider_data_layer.query(return_count_only=True)

5943659

Check Geocoding¶

Let's do a random test to check geocoded data. For a given state, we will look at and plot the data points based on address provided vs address geocoded.

Geocoding Check: Wyoming¶

# Create a spatially enabled dataframe for WY
%time wy_df = provider_data_layer.query(where="user_Region='WY'", as_df=True)
# %time wy_df = provider_data_layer.query(where="USER_Provider_Business_Practice_Location_Address_State_Name='WY'", as_df=True)

wy_df.shape

Wall time: 54.3 s

(12770, 91)

# Look at data points that are geocoded outside of Wyoming
len(wy_df[wy_df['region']!='Wyoming'])

3

# Check the accuracy of geocoding
round(100-((wy_df.shape[1]/wy_df.shape[0])*100),2)

99.29

map1 = gis.map('USA')
map1

# Plot data points for WY
wy_df.spatial.plot(map1)

True

map1.take_screenshot()

map1.remove_layers()

True

We have an accuracy of >99% with geocoding. From this map, we can see that out of 12770 points that have been geocoded only 3 are outside of WY. Similar analysis can be performed for other states to check for geocoding errors.

Geocoding Check: Arizona¶

# Create a spatially enabled dataframe for AZ
# %time az_df = provider_data_layer.query(where="user_Region='AZ'", as_df=True)
%time az_df = provider_data_layer.query(where="USER_Provider_Business_Practice_Location_Address_State_Name='AZ'", as_df=True)

az_df.shape

Wall time: 19min

(108774, 368)

# Look at data points that are geocoded outside of Arizona
len(az_df[az_df['Region']!='Arizona'])

7582

Geocoding Check: Texas¶

# Create a spatially enabled dataframe for TX
%time tx_df = provider_data_layer.query(where="user_Region='TX'", as_df=True)
tx_df.shape

Wall time: 4min

(365783, 93)

# Look at data points that are geocoded outside of Texas
len(tx_df[tx_df['Region']!='Texas'])

17

Data Exploration¶

Heatmap of Providers in U.S.¶

To visualize how healthcare providers are distributed, let's do a heatmap of providers in the United States.

map_usa = gis.map('USA')
map_usa

# Add provider data and create a heatmap
usa_layer = FeatureLayer("https://datascienceqa.esri.com/server/rest/services/Hosted/provider_clean_data_geocoded_6_19/FeatureServer")
renderer = {"renderer": "autocast", #This tells python to use JS autocasting
            "type": "heatmap",
#             "minScale":100,
#             "maxScale":10,
            "blurRadius":1,  # changes the size of the clusters
            "maxPixelIntensity":2,
            "minPixelIntensity":0,
            "field":None}
renderer["colorStops"] = [{"ratio":0,"color":[63, 40, 102, 0]},
                          {"ratio":0.25,"color":[167,97,170,179]},
                          {"ratio":0.50,"color":"#7b3ce9"},
                          {"ratio":0.75,"color":[222,102,0,179]},
                          {"ratio":1,"color":[244,204,0,179]}]
#                           {"ratio":1,"color":"#ffff00"}]
map_usa.add_layer(usa_layer,
               { "type": "FeatureLayer",
                 "renderer": renderer,
                })

map_usa.legend = True

map_usa.remove_layers()

True

map_usa.take_screenshot(set_as_preview=True)

The map shows distribution of providers throughout the US. We can see large gaps in Nevada, Idaho, Utah, Wyoming and Texas. These and other states can be explored further for analysis.

The Lone Star State - Exploring Texas¶

The second largest state in U.S. both by area and population, Texas baosts of 261,231.71 sq mi land area with a population of ~28.7 million. The population density is low with just 105.2 people per square mile and varies drastically between heavely populated urban areas to sparsely populated rural.

Image source: https://www.npr.org/sections/thetwo-way/2018/05/24/614195884/new-census-data-shows-texas-cities-are-growing-faster-than-all-other-states

As seen in the previous notebook, Texas has the fourth largest number of healthcare providers in U.S. However, the state stands second highest on Health Resources and Services Administration's (HRSA) list of shortage areas. Texas also tops HRSA's list of medically underserved areas.

Let's start our journey with Texas!

Providers and Population Density¶

# Search for Population data layer
popsearch_result = gis.content.search('title: 2018 USA Population Density')
popsearch_result

[<Item title:"2018 USA Tapestry Segmentation" type:Map Image Layer owner:esri_livingatlas>,
 <Item title:"2018 USA Population Density" type:Map Image Layer owner:esri_livingatlas>]

# Get Population Density
popdensity = popsearch_result[1]
popdensity

Let's bring the 2018 USA Population Density and Provider Data layers together to explore providers with respect to population density.

# Create Map
tx_pop_map = gis.map('USA')
tx_pop_map

We will use ClassedColorRenderer, a Smart Mapping feature, to automatically generate color classes for population density. The renderer is then applied to different State, City, County, Tract and Block Group layers of the population density layer.

# Add population density for Texas
for layer in popdensity.layers:
#     if "43" in layer.url or "44" in layer.url:
    if layer.properties['id'] in [43,44,45,46,47,48,49]:
        tx_pop_map.add_layer(layer, {  "type": "FeatureLayer",
                                 "renderer":"ClassedColorRenderer",
#                                  "definition_expression" : "STATE_NAME='Utah' or STATE_NAME='Nevada' or STATE_NAME='Idaho' or STATE_NAME='Wyoming'",
                                 "definition_expression" : "STATE_NAME='Texas'",
                                 "opacity":0.7,
                                 "field_name":"POPDENS_CY"})

We will now plot healthcare providers as points on top of the population density layer.

# Add provider data layer for Texas
tx_pop_map.add_layer(provider_data_layer,
               { "type": "FeatureLayer",
                "definition_expression" : "Region = 'Texas'",
                "opacity":0.7})

# Add Legend
tx_pop_map.legend = True

tx_pop_map.remove_layers()

True

tx_pop_map.take_screenshot()

Providers and Median Income¶

Let's bring the 2018 USA Median Household Income and Provider Data layers together to explore providers with respect to median income.

# Create Map
tx_income_map = gis.map('USA', 6)
tx_income_map

We will use ClassedColorRenderer, a Smart Mapping feature, to automatically generate color classes for median income. The renderer is then applied to different State, City, County, Tract and Block Group levels. We will use the median household income variable of the Census Data layer for this analysis.

# Add Median Household Income for Texas
for layer in popdensity.layers:
    if layer.properties['id'] in [43,44,45,46,47,48,49]:
        tx_income_map.add_layer(layer, {  "type": "FeatureLayer",
                                 "renderer":"ClassedColorRenderer",
                                 "definition_expression" : "STATE_NAME='Texas'",
                                 "opacity":0.7,
                                 "field_name":"MEDHINC_CY"})

# Add provider data layer for Texas
tx_income_map.add_layer(provider_data_layer,
               { "type": "FeatureLayer",
                "definition_expression" : "Region = 'Texas'",
                "opacity":0.7})

# Add Legend
tx_income_map.legend = True

tx_income_map.remove_layers()

True

tx_income_map.take_screenshot()

Providers and Median Age¶

Let's bring the 2018 USA Median Age and Provider Data layers together to explore providers with respect to median age.

# Create Map
tx_age_map = gis.map('USA', 6)
tx_age_map

We will use ClassedColorRenderer, a Smart Mapping feature, to automatically generate color classes for median income. The renderer is then applied to different State, City, County, Tract and Block Group levels. We will use the median age for current year (2018) variable of the Census Data layer for this analysis.

# Add Median Age for Texas
for layer in popdensity.layers:
#     if "43" in layer.url or "44" in layer.url:
    if layer.properties['id'] in [43,44,45,46,47,48,49]:
        tx_age_map.add_layer(layer, {  "type": "FeatureLayer",
                                 "renderer":"ClassedColorRenderer",
                                 "definition_expression" : "STATE_NAME='Texas'",
                                 "opacity":0.7,
                                 "field_name":"MEDAGE_CY"})

# Add provider data layer for Texas
tx_age_map.add_layer(provider_data_layer,
               { "type": "FeatureLayer",
                "definition_expression" : "Region = 'Texas'",
                "opacity":0.7})

# Add Legend
tx_age_map.legend = True

tx_age_map.remove_layers()

True

tx_age_map.take_screenshot()

Providers and Percent of Total Population with no Health Insurance Coverage¶

Let's bring the Health Insurance Coverage Data and Provider Data layers together to explore providers with respect to percent of total population with no Health Insurance Coverage.

# Create Map
tx_insurance_map = gis.map('USA')
tx_insurance_map

This map shows a troubling picture of numerous counties with very few provders where percentage of population with no health insurance is among the highest in Texas. Some of the counties include:

South and Southwest Texas: Frio, La Salle, Dimmit, Webb, Maverick, Kennedy, Willacy, Starr
Central Texas: Val Verde, Crockett, Regan, Stephens, Eastland, Callahan
West Texas: Hudspeth, Presidio, Winkler, Ector
North: Dallam, Sherman, Potter, Bailey, Cochran

We will use ClassedColorRenderer, a Smart Mapping feature, to automatically generate color classes for population with no health insurance. The renderer is then applied to different State, County and Tract layers of data.

# Add Health Insurance Coverage layers for State, County and Tract
from arcgis.mapping import MapImageLayer
tx_insurance_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer")
tx_insurance_county = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer/1")
tx_insurance_tract = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Health_Insurance_Boundaries/FeatureServer/2")

tx_insurance_map.add_layer(tx_insurance_state,
               { "type": "FeatureLayer",
                "definition_expression" : "Name = 'Texas'",
                "field_name":"B27010_calc_pctNoInsE",
                "renderer":"ClassedColorRenderer",
                "opacity":0.7})

tx_insurance_map.add_layer(tx_insurance_county,
               { "type": "FeatureLayer",
                "definition_expression" : "STATE = 'Texas'",
                "renderer":"ClassedColorRenderer",
                "field_name":"B27010_calc_pctNoInsE",
                "opacity":0.7})

tx_insurance_map.add_layer(tx_insurance_tract,
               { "type": "FeatureLayer",
                "definition_expression" : "State = 'Texas'",
                "renderer":"ClassedColorRenderer",
                "field_name":"B27010_calc_pctNoInsE",
                "opacity":0.7})

# Add provider data layer for Texas
tx_insurance_map.add_layer(provider_data_layer,
               { "type": "FeatureLayer",
                "definition_expression" : "Region = 'Texas'",
                "opacity":0.7})

# Add Legend
tx_insurance_map.legend = True

tx_insurance_map.remove_layers()

True

tx_insurance_map.take_screenshot()

state_layer.properties

---------------------------------------------------------------------------
gaierror                                  Traceback (most recent call last)
~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args)
   1316                 h.request(req.get_method(), req.selector, req.data, headers,
-> 1317                           encode_chunked=req.has_header('Transfer-encoding'))
   1318             except OSError as err: # timeout error

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in request(self, method, url, body, headers, encode_chunked)
   1228         """Send a complete request to the server."""
-> 1229         self._send_request(method, url, body, headers, encode_chunked)
   1230 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in _send_request(self, method, url, body, headers, encode_chunked)
   1274             body = _encode(body, 'body')
-> 1275         self.endheaders(body, encode_chunked=encode_chunked)
   1276 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in endheaders(self, message_body, encode_chunked)
   1223             raise CannotSendHeader()
-> 1224         self._send_output(message_body, encode_chunked=encode_chunked)
   1225 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in _send_output(self, message_body, encode_chunked)
   1015         del self._buffer[:]
-> 1016         self.send(msg)
   1017 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in send(self, data)
    955             if self.auto_open:
--> 956                 self.connect()
    957             else:

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in connect(self)
   1383 
-> 1384             super().connect()
   1385 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in connect(self)
    927         self.sock = self._create_connection(
--> 928             (self.host,self.port), self.timeout, self.source_address)
    929         self.sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\socket.py in create_connection(address, timeout, source_address)
    706     err = None
--> 707     for res in getaddrinfo(host, port, 0, SOCK_STREAM):
    708         af, socktype, proto, canonname, sa = res

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\socket.py in getaddrinfo(host, port, family, type, proto, flags)
    747     addrlist = []
--> 748     for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
    749         af, socktype, proto, canonname, sa = res

gaierror: [Errno 11001] getaddrinfo failed

During handling of the above exception, another exception occurred:

URLError                                  Traceback (most recent call last)
<ipython-input-104-c9a48188431f> in <module>
----> 1 state_layer.properties

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\gis\__init__.py in properties(self)
   9533             return self._lazy_properties
   9534         else:
-> 9535             self._hydrate()
   9536             return self._lazy_properties
   9537 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\gis\__init__.py in _hydrate(self)
   9552                 else:
   9553                     if isinstance(self._con, arcgis._impl._ArcGISConnection):
-> 9554                         self._lazy_token = self._con.generate_portal_server_token(self._url)
   9555                     else:
   9556                         self._lazy_token = self._con.token

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\connection.py in generate_portal_server_token(self, serverUrl, expiration)
    533             if self.baseurl.endswith('/'):
    534                 resp = self.post('generateToken', postdata,
--> 535                                  ssl=True, add_token=False)
    536             else:
    537                 resp = self.post('/generateToken', postdata,

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\connection.py in post(self, path, postdata, files, ssl, compress, is_retry, use_ordered_dict, add_token, verify_cert, token, try_json, out_folder, file_name, force_bytes, add_headers)
   1123             opener.addheaders = headers
   1124             #print("***"+url)
-> 1125             resp = opener.open(url, data=encoded_postdata.encode())
   1126             resp_data, is_file = self._process_response(resp,
   1127                                                out_folder=out_folder,

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in open(self, fullurl, data, timeout)
    523             req = meth(req)
    524 
--> 525         response = self._open(req, data)
    526 
    527         # post-process response

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in _open(self, req, data)
    541         protocol = req.type
    542         result = self._call_chain(self.handle_open, protocol, protocol +
--> 543                                   '_open', req)
    544         if result:
    545             return result

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in _call_chain(self, chain, kind, meth_name, *args)
    501         for handler in handlers:
    502             func = getattr(handler, meth_name)
--> 503             result = func(*args)
    504             if result is not None:
    505                 return result

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in https_open(self, req)
   1358         def https_open(self, req):
   1359             return self.do_open(http.client.HTTPSConnection, req,
-> 1360                 context=self._context, check_hostname=self._check_hostname)
   1361 
   1362         https_request = AbstractHTTPHandler.do_request_

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args)
   1317                           encode_chunked=req.has_header('Transfer-encoding'))
   1318             except OSError as err: # timeout error
-> 1319                 raise URLError(err)
   1320             r = h.getresponse()
   1321         except:

URLError: <urlopen error [Errno 11001] getaddrinfo failed>

from arcgis.geoanalytics import summarize_data
provider_state_agg = summarize_data.aggregate_points(point_layer = provider_data_layer,
                                            polygon_layer = state_layer,
                                            output_name='provider_state_agg',
                                            summary_fields=[{"statisticType": "Count", "onStatisticField": "user_npi"}])

---------------------------------------------------------------------------
gaierror                                  Traceback (most recent call last)
~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args)
   1316                 h.request(req.get_method(), req.selector, req.data, headers,
-> 1317                           encode_chunked=req.has_header('Transfer-encoding'))
   1318             except OSError as err: # timeout error

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in request(self, method, url, body, headers, encode_chunked)
   1228         """Send a complete request to the server."""
-> 1229         self._send_request(method, url, body, headers, encode_chunked)
   1230 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in _send_request(self, method, url, body, headers, encode_chunked)
   1274             body = _encode(body, 'body')
-> 1275         self.endheaders(body, encode_chunked=encode_chunked)
   1276 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in endheaders(self, message_body, encode_chunked)
   1223             raise CannotSendHeader()
-> 1224         self._send_output(message_body, encode_chunked=encode_chunked)
   1225 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in _send_output(self, message_body, encode_chunked)
   1015         del self._buffer[:]
-> 1016         self.send(msg)
   1017 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in send(self, data)
    955             if self.auto_open:
--> 956                 self.connect()
    957             else:

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in connect(self)
   1383 
-> 1384             super().connect()
   1385 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\http\client.py in connect(self)
    927         self.sock = self._create_connection(
--> 928             (self.host,self.port), self.timeout, self.source_address)
    929         self.sock.setsockopt(socket.IPPROTO_TCP, socket.TCP_NODELAY, 1)

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\socket.py in create_connection(address, timeout, source_address)
    706     err = None
--> 707     for res in getaddrinfo(host, port, 0, SOCK_STREAM):
    708         af, socktype, proto, canonname, sa = res

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\socket.py in getaddrinfo(host, port, family, type, proto, flags)
    747     addrlist = []
--> 748     for res in _socket.getaddrinfo(host, port, family, type, proto, flags):
    749         af, socktype, proto, canonname, sa = res

gaierror: [Errno 11001] getaddrinfo failed

During handling of the above exception, another exception occurred:

URLError                                  Traceback (most recent call last)
<ipython-input-101-b45a3f4a1ef5> in <module>
      3                                             polygon_layer = state_layer,
      4                                              output_name='provider_state_agg',
----> 5                                             summary_fields=[{"statisticType": "Count", "onStatisticField": "user_npi"}])

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\geoanalytics\summarize_data.py in aggregate_points(point_layer, bin_type, bin_size, bin_size_unit, polygon_layer, time_step_interval, time_step_interval_unit, time_step_repeat_interval, time_step_repeat_interval_unit, time_step_reference, summary_fields, output_name, gis)
    283         output_service_name = output_name.replace(' ', '_')
    284 
--> 285     output_service = _create_output_service(gis, output_name, output_service_name, 'Aggregate Points')
    286 
    287     params['output_name'] = _json.dumps({

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\geoanalytics\_util.py in _create_output_service(gis, output_name, output_service_name, task)
     80 
     81 def _create_output_service(gis, output_name, output_service_name='Analysis feature service', task='GeoAnalytics'):
---> 82     ok = gis.content.is_service_name_available(output_name, 'Feature Service')
     83     if not ok:
     84         raise RuntimeError("A Feature Service by this name already exists: " + output_name)

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\gis\__init__.py in is_service_name_available(self, service_name, service_type)
   4111         }
   4112 
-> 4113         res = self._portal.con.post(path, postdata)
   4114         return res['available']
   4115 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\connection.py in post(self, path, postdata, files, ssl, compress, is_retry, use_ordered_dict, add_token, verify_cert, token, try_json, out_folder, file_name, force_bytes, add_headers)
   1123             opener.addheaders = headers
   1124             #print("***"+url)
-> 1125             resp = opener.open(url, data=encoded_postdata.encode())
   1126             resp_data, is_file = self._process_response(resp,
   1127                                                out_folder=out_folder,

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in open(self, fullurl, data, timeout)
    523             req = meth(req)
    524 
--> 525         response = self._open(req, data)
    526 
    527         # post-process response

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in _open(self, req, data)
    541         protocol = req.type
    542         result = self._call_chain(self.handle_open, protocol, protocol +
--> 543                                   '_open', req)
    544         if result:
    545             return result

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in _call_chain(self, chain, kind, meth_name, *args)
    501         for handler in handlers:
    502             func = getattr(handler, meth_name)
--> 503             result = func(*args)
    504             if result is not None:
    505                 return result

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in https_open(self, req)
   1358         def https_open(self, req):
   1359             return self.do_open(http.client.HTTPSConnection, req,
-> 1360                 context=self._context, check_hostname=self._check_hostname)
   1361 
   1362         https_request = AbstractHTTPHandler.do_request_

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\urllib\request.py in do_open(self, http_class, req, **http_conn_args)
   1317                           encode_chunked=req.has_header('Transfer-encoding'))
   1318             except OSError as err: # timeout error
-> 1319                 raise URLError(err)
   1320             r = h.getresponse()
   1321         except:

URLError: <urlopen error [Errno 11001] getaddrinfo failed>

Mental Health¶

Image source: https://cuindependent.com/2019/03/08/opinion-mental-health-education/

Nearly one in five people in the U.S. have some sort of mental health condition.
Adults living with serious mental illness die on average 25 years earlier than others.
Suicide is the 2nd leading cause of death for people aged 10–34.

Mental Healthcare Providers - Heat Map¶

Let's explore the distribution of mental health providers in US using a heatmap.

mental_map = gis.map('USA')
mental_map

This map paints a grim picture of the availability of mental healthcare providers. There are states in every region with vast areas of NO mental health providers or very few providers. To list, some of these states include:

Midwest: The Dakotas, Montana, Wyoming, Colorado, Nebraska, Kansas
South: Texas, New Mexico, Kansas, Louisiana, Mississippi, Alabama
West: Nevada, Arizona, Oregon, Idaho, Alaska
East: Maine, Vermont, Michigan

renderer = {"renderer": "autocast", #This tells python to use JS autocasting
            "type": "heatmap",
            "blurRadius":1,  # changes the size of the clusters
            "maxPixelIntensity":2,
            "minPixelIntensity":0,
            "field":None}
renderer["colorStops"] = [{"ratio":0,"color":[63, 40, 102, 0]},
                          {"ratio":0.25,"color":[167,97,170,179]},
                          {"ratio":0.50,"color":"#7b3ce9"},
                          {"ratio":0.75,"color":[222,102,0,179]},
                          {"ratio":1,"color":[244,204,0,179]}]
mental_map.add_layer(provider_data_layer,
               { "type": "FeatureLayer",
                 "renderer": renderer,
                "definition_expression" : "user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X']"
               })

mental_map.remove_layers()

True

mental_map.take_screenshot()

Mental Healthcare Providers and Population Density¶

Let's get census data from USA_Demographics_and_Boundaries_2018 layer. We will use 'Pop per Square Mile' as the variable to create our basemap and add providers as points on top.

# Search for Population data layer
popsearch_result = gis.content.search('title: 2018 USA Population Density')
popsearch_result

[<Item title:"2018 USA Tapestry Segmentation" type:Map Image Layer owner:esri_livingatlas>,
 <Item title:"2018 USA Population Density" type:Map Image Layer owner:esri_livingatlas>]

# Get Population Density
popdensity = popsearch_result[1]
popdensity

mental_map2 = gis.map('USA')
mental_map2

From this map, we can see that percent of women (15 to 50) who had a birth in the past 12 months is high in Idaho, Utah, North Dakota, South Dakota and Nebraska.

# Add population density layer
for layer in popdensity.layers:
#     if "43" in layer.url or "44" in layer.url:
    if layer.properties['id'] in [43,44,45,46,47,48,49]:
        mental_map2.add_layer(layer, {  "type": "FeatureLayer",
                                 "renderer":"ClassedColorRenderer",
# #                                  "definition_expression" : "STATE_NAME='Utah' or STATE_NAME='Nevada' or STATE_NAME='Idaho' or STATE_NAME='Wyoming'",
#                                  "definition_expression" : "STATE_NAME='Texas'",
                                 "opacity":0.7,
                                 "field_name":"POPDENS_CY"})

# Add Mental Healthcare Provider Data
mental_map2.add_layer(provider_data_layer,
                       {"type":"FeatureLayer",
                        "definition_expression":"user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X']",
                        "opacity":0.7})

mental_map2.remove_layers()

True

mental_map2.legend = True

mental_map2.take_screenshot()

popdensity.layers

[<FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/0">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/1">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/2">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/3">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/4">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/5">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/6">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/7">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/8">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/9">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/10">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/11">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/12">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/13">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/14">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/15">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/16">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/17">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/18">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/19">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/20">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/21">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/22">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/23">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/24">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/25">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/26">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/27">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/28">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/29">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/30">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/31">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/32">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/33">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/34">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/35">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/36">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/37">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/38">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/39">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/40">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/41">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/42">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/43">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/44">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/45">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/46">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/47">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/48">,
 <FeatureLayer url:"https://datascienceqa.esri.com/portal/sharing/servers/ab4e1996d588405d9cd68348ef660f70/rest/services/USA_Demographics_and_Boundaries_2018/MapServer/49">]

Population to Providers Ratio by State¶

Let's find out the ratio of population to mental healthcare providers to understand which states have the least number of providers.

Dataframe for population by State¶

We will use the fertility layer at state level to create this dataframe

# State population dataframe
state_layer = popdensity.layers[43]
state_featureset = state_layer.query(out_fields='STATE_NAME,ST_ABBREV,TOTPOP_CY')
state_df = state_featureset.sdf
state_df.head()

Dataframe for Mental Healthcare providers by state¶

We will use provider_data_layer to subset mental healthcare providers

# Get provider data for mental healthcare providers only

mental_featureset = provider_data_layer.query(where="user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X']", 
                          out_fields='user_npi,user_entity_type,user_provider_gender,user_taxonomy_code_1,user_full_address,Postal,City,Subregion,Region,RegionAbbr')
mental_df = mental_featureset.sdf
mental_df.head()

# Create dataframe of mental healthcare provider counts by state
mental_count_df = pd.DataFrame(mental_df['RegionAbbr'].value_counts().reset_index().values, columns=['RegionAbbr','Provider_Count'])

# Plot mental healthcare Providers by State

plt.figure(figsize=(25,12))
sns.barplot(mental_count_df['RegionAbbr'].iloc[:-19], mental_count_df['Provider_Count'].iloc[:-19])
plt.title('Mental Healthcare Providers by State', fontsize=22)
plt.xlabel('States', fontsize=18)
plt.ylabel('Provider Count', fontsize=18)

Text(0, 0.5, 'Provider Count')

Merge Provider Count and Population Dataframes¶

# Merge provider count and women_df at state level
state_mental_df = pd.merge(mental_count_df,state_df,left_on='RegionAbbr', right_on='ST_ABBREV',how='inner')

state_mental_df.head()

# Create new columns that shows people per provider
state_mental_df['people_per_prov'] = state_mental_df['TOTPOP_CY']/state_mental_df['Provider_Count']

# Arrange dataframe by mother_per_prov descending
state_mental_df = state_mental_df.sort_values(by=['people_per_prov'], ascending=False)
state_mental_df.head()

# Plot No. of Mothers (age 15 to 50) per OB-GYN Provider by State

plt.figure(figsize=(25,12))
sns.barplot(state_mental_df['RegionAbbr'], state_mental_df['people_per_prov'])
plt.title('No. of People per Mental Heathcare Provider by State', fontsize=22)
plt.xlabel('States', fontsize=18)
plt.ylabel('No. of People', fontsize=18)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)

(array([   0.,  500., 1000., 1500., 2000., 2500., 3000.]),
 <a list of 7 Text yticklabel objects>)

On average, there were ~2754 people per mental healthcare provider in Mississippi compared to ~364 people per provider in DC. The difference is drastic.

Exploring Mississippi¶

Mississippi has the highest number of people per mental healthcare provider. Let's explore Mississippi to find out which counties have the lowest number of providers.

Dataframe for Population in Mississippi¶

# County population df
county_layer = popdensity.layers[46]
MS_featureset = county_layer.query(where="ST_ABBREV='MS'", out_fields='ST_ABBREV,NAME,TOTPOP_CY')
MS_pop_df = MS_featureset.sdf
MS_pop_df.head()

Dataframe for Mental Healthcare Providers in Mississippi by County¶

# Get provider data for obgyn providers only
mental_MS_df = mental_df[mental_df['RegionAbbr']=='MS']
mental_MS_df.head()
mental_MS_df.shape

(1108, 12)

# Create dataframe of obgyn provider counts by county
mental_MScounty_df = pd.DataFrame(mental_MS_df['Subregion'].value_counts().reset_index().values, columns=['County','Provider_Count'])
mental_MScounty_df.head()

mental_MScounty_df

Merge Provider Count and Population Dataframes¶

# Merge provider count and women data at county level for ND
county_mental_df = pd.merge(MS_pop_df,mental_MScounty_df,left_on='NAME', right_on='County',how='left')

county_mental_df.head()

# Create new columns that shows Mental Healthcare provider by population
county_mental_df['people_per_prov'] = county_mental_df['TOTPOP_CY']/county_mental_df['Provider_Count']

# Arrange dataframe by mother_per_prov descending and then B13016_002E (women who gave birht) descending 
county_mental_df = county_mental_df.sort_values(by=['people_per_prov'], ascending=False)[['NAME','TOTPOP_CY','Provider_Count','people_per_prov']]
# county_obgyn_df.columns = ['Name','Provider_Count', "Total Women (15 to 50)","Women who had birth (past 12 months)", 'women_per_prov', 'mother_per_prov']
county_mental_df

18 counties in ND do not have any mental healthcare provider.
Among counties with providers, Marshall County seems to be the worst with 1 provider for 37137 people.

TO be Deleted?¶

Mental Health - MI

mental_map_mi = gis.map('USA')
mental_map_mi

# Add population density for Michigan
for layer in popdensity.layers:
#     if "43" in layer.url or "44" in layer.url:
    if layer.properties['id'] in [43,44,45,46,47,48,49]:
        mental_map_mi.add_layer(layer, {  "type": "FeatureLayer",
                                 "renderer":"ClassedColorRenderer",
#                                  "definition_expression" : "STATE_NAME='Utah' or STATE_NAME='Nevada' or STATE_NAME='Idaho' or STATE_NAME='Wyoming'",
                                 "definition_expression" : "STATE_NAME='Michigan'",
                                 "opacity":0.7,
                                 "field_name":"POPDENS_CY"})

# provider_layer = FeatureLayer("https://datascienceqa.esri.com/server/rest/services/Hosted/provider_clean_data_geocoded_6_19/FeatureServer")
mental_map_mi.add_layer(provider_data_layer,
                       {"type":"FeatureLayer",
                        "definition_expression":"Region='Michigan' and (user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X'])",
#                         "definition_expression":"Region='Michigan'",
                        "opacity":0.7})

mental_map_mi.legend = True

mental_map_mi.remove_layers()

True

mental_map_mi.take_screenshot()

Mental Map - 4 states

mid_states_map = gis.map('USA')
mid_states_map

# Add population density for mid states
for layer in popdensity.layers:
#     if "43" in layer.url or "44" in layer.url:
    if layer.properties['id'] in [43,44,45,46,47,48,49]:
        mid_states_map.add_layer(layer, {  "type": "FeatureLayer",
                                 "renderer":"ClassedColorRenderer",
                                 "definition_expression" : "STATE_NAME='Utah' or STATE_NAME='Nevada' or STATE_NAME='Idaho' or STATE_NAME='Wyoming' or STATE_NAME='Montana' or STATE_NAME='Colorado'",
#                                  "definition_expression" : "STATE_NAME='Michigan'",                                 
                                 "opacity":0.7,
                                 "field_name":"POPDENS_CY"})

mid_states_map.add_layer(provider_data_layer,
                       {"type":"FeatureLayer",
                        "definition_expression":"(Region in ['Utah','Wyoming','Nevada','Idaho','Montana','Colorado']) and (user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X'])",
#                         "definition_expression":"Region='Michigan'",
                        "opacity":0.7})

mid_states_map.legend = True

mid_states_map.remove_layers()

True

mid_states_map.take_screenshot()

Southern States¶

south_states_map = gis.map('USA')
# south_states_map.basemap = 'national-geographic'
south_states_map

# Add population density for mid states
for layer in popdensity.layers:
#     if "43" in layer.url or "44" in layer.url:
    if layer.properties['id'] in [43,44,45,46,47,48,49]:
        south_states_map.add_layer(layer, {  "type": "FeatureLayer",
                                 "renderer":"ClassedColorRenderer",
                                 "definition_expression" : "STATE_NAME='Arkansas' or STATE_NAME='Mississippi' or STATE_NAME='Louisiana' or STATE_NAME='Tennessee' or STATE_NAME='North Carolina' or STATE_NAME='South Carolina' or STATE_NAME='Alabama' or STATE_NAME='Georgia' or STATE_NAME='Florida'",
#                                  "definition_expression" : "STATE_NAME='Michigan'",                                 
                                 "opacity":0.5,
                                 "field_name":"POPDENS_CY"})

south_states_map.add_layer(provider_data_layer,
                       {"type":"FeatureLayer",
                        "definition_expression":"(Region in ['Arkansas','Mississippi','Louisiana','Tennessee','North Carolina','South Carolina','Alabama','Georgia','Florida']) and (user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X'])",
#                         "definition_expression":"Region='Michigan'",
                        "opacity":0.7})

south_states_map.legend = True

south_states_map.remove_layers()

True

south_states_map.take_screenshot()

Top States¶

top_states_map = gis.map('USA')
# south_states_map.basemap = 'national-geographic'
top_states_map

# Add population density for mid states
for layer in popdensity.layers:
#     if "43" in layer.url or "44" in layer.url:
    if layer.properties['id'] in [43,44,45,46,47,48,49]:
        top_states_map.add_layer(layer, {  "type": "FeatureLayer",
                                 "renderer":"ClassedColorRenderer",
                                 "definition_expression" : "STATE_NAME='Minnesota' or STATE_NAME='Iowa' or STATE_NAME='North Dakota' or STATE_NAME='South Dakota'",
#                                  "definition_expression" : "STATE_NAME='Michigan'",                                 
                                 "opacity":0.5,
                                 "field_name":"POPDENS_CY"})

top_states_map.add_layer(provider_data_layer,
                       {"type":"FeatureLayer",
                        "definition_expression":"(Region in ['Minnesota','Iowa','North Dakota','South Dakota']) and (user_taxonomy_code_1 in ['2084P0800X','207QG0300X','273R00000X','103T00000X','103TA0400X','103TA0700X','103TC0700X','103TC2200X','103TB0200X','103TC1900X','103TE1000X','103TE1100X','103TF0000X','103TF0200X','103TP2701X','103TH0004X','103TH0100X','103TM1700X','103TM1800X','103TP0016X','103TP0814X','103TP2700X','103TR0400X','103TS0200X','103TW0100X','106E00000X','106S00000X','2084A0401X','2084P0802X','2084B0002X','2084P0804X','2084N0600X','2084D0003X','2084F0202X','2084P0805X','2084H0002X','2084P0005X','2084N0400X','2084N0402X','2084N0008X','2084P2900X','2084P0015X','2084S0012X','2084S0010X','2084V0102X','364SP0808X','364SP0809X','364SP0807X','364SP0810X','364SP0811X','364SP0812X','364SP0813X','283Q00000X','261QM0801X'])",
#                         "definition_expression":"Region='Michigan'",
                        "opacity":0.7})

top_states_map.legend = True

top_states_map.remove_layers()

True

top_states_map.take_screenshot()

Women's Health¶

Half of U.S. counties lack a single OB-GYN and women’s lives are endangered by long treks for much-needed care.
With a severe shortage of obstetrician/gynecologists forecasted, some metropolitan areas are prone to crisis-level conditions.
By 2020, there will be a shortage of up to 8,800 OB-GYNs, and the shortage may grow upto 22,000 by 2050.

Let's explore the distribution of OBGYN health providers in US using a heatmap.

Note - Provider Taxonomy codes were filtered for OB-GYN providers using this reference.

OBGYN Providers - Heat Map¶

women_map = gis.map('USA')
women_map

This map paints a grim picture of the availability of OBGYN healthcare providers. We can see vast areas in Midwest and West with NO or very few OBGYN providers.

# Define renderer and add provider data for OBGYN providers
renderer = {"renderer": "autocast", #This tells python to use JS autocasting
            "type": "heatmap",
            "blurRadius":1,  # changes the size of the clusters
            "maxPixelIntensity":2,
            "minPixelIntensity":0,
            "field":None}
renderer["colorStops"] = [{"ratio":0,"color":[63, 40, 102, 0]},
                          {"ratio":0.25,"color":[167,97,170,179]},
                          {"ratio":0.50,"color":"#7b3ce9"},
                          {"ratio":0.75,"color":[222,102,0,179]},
                          {"ratio":1,"color":[244,204,0,179]}]
women_map.add_layer(provider_data_layer,
               { "type": "FeatureLayer",
                 "renderer": renderer,
                "definition_expression" : "user_taxonomy_code_1 in ('207V00000X','207VC0200X','207VF0040X','207VX0201X','207VG0400X','207VH0002X','207VM0101X','207VB0002X','207VX0000X','207VE0102X','363LX0001X','163WR1000X','163WW0101X','282NW0100X')"
#                 "definition_expression" : "user_taxonomy_code_1 = '207VC0200X' or user_taxonomy_code_1 = '207V00000X'"
                })

# Remove Layer
women_map.remove_layers()

True

# Take Screenshot
women_map.take_screenshot()

OB-GYN Providers and Fertility¶

Let's get data from the American Community Survey (ACS) about fertility in past 12 months by age of mother using ACS_Fertility_by_Age_Boundaries layer at State, County and Tract level. We will use 'Percent of women age 15 to 50 who had a birth in the past 12 months' as the variable to create our basemap and add providers as points on top.

# Get the Fertility Layers
fertility_search = gis.content.search('title: ACS_Fertility_by_Age_Boundaries', 'Feature Layer')
fertility_item = fertility_search[0]
fertility_item.layers

[<FeatureLayer url:"https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/0">,
 <FeatureLayer url:"https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/1">,
 <FeatureLayer url:"https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/2">]

# Define layers for State, County and Tract (Percent of women 15 to 50 who had a birth in the past 12 months)
fertility_state = fertility_item.layers[0]
fertility_county = fertility_item.layers[1]
fertility_tract = fertility_item.layers[2]

fertility_map = gis.map('USA')
fertility_map

From this map, we can see that percent of women (15 to 50) who had a birth in the past 12 months is high in Idaho, Utah, North Dakota, South Dakota and Nebraska.

# Add Fertility layers for State, County and Tract (Percent of women 15 to 50 who had a birth in the past 12 months)
from arcgis.mapping import MapImageLayer
fertility_map.add_layer(fertility_state,
               { "type": "FeatureLayer",
                "field_name":"B13016_calc_pctBirthsE",
                "renderer":"ClassedColorRenderer",
                "opacity":0.5})

fertility_map.add_layer(fertility_county,
               { "type": "FeatureLayer",
                "renderer":"ClassedColorRenderer",
                "field_name":"B13016_calc_pctBirthsE",
                "opacity":0.5})

fertility_map.add_layer(fertility_tract,
               { "type": "FeatureLayer",
                "renderer":"ClassedColorRenderer",
                "field_name":"B13016_calc_pctBirthsE",
                "opacity":0.5})

# Add OB-GYN Provider Data
fertility_map.add_layer(provider_data_layer,
                       {"type":"FeatureLayer",
                        "definition_expression":"user_taxonomy_code_1 in ['207V00000X','207VC0200X','207VF0040X','207VX0201X','207VG0400X','207VH0002X','207VM0101X','207VB0002X','207VX0000X','207VE0102X','363LX0001X','163WR1000X','163WW0101X','282NW0100X']",
                        "opacity":0.7})

fertility_map.remove_layers()

True

fertility_map.legend = True

fertility_map.take_screenshot()

Mothers to Providers Ratio by State¶

Let's find out the ratio of mothers to OBGYN providers to understand which states have the least number of OB-GYN providers

Dataframe for women population by State¶

We will use the fertility layer at state level to create this dataframe

# State population dataframe
fertility_featureset = fertility_state.query(where="B13016_001E>1")
fertility_df = fertility_featureset.sdf
fertility_df.head()

Dataframe for OB-GYN providers by state¶

We will use provider_data_layer to subset OB-GYN providers

# Get provider data for obgyn providers only

obgyn_featureset = provider_data_layer.query(where="user_taxonomy_code_1 in ('207V00000X','207VC0200X','207VF0040X','207VX0201X','207VG0400X','207VH0002X','207VM0101X','207VB0002X','207VX0000X','207VE0102X','363LX0001X','163WR1000X','163WW0101X','282NW0100X')", out_fields='user_npi,user_entity_type,user_provider_gender,user_taxonomy_code_1,user_full_address,postal,city,subregion,region,regionabbr')
obgyn_df = obgyn_featureset.sdf
obgyn_df.head()

# Create dataframe of obgyn provider counts by state
obgyn_count_df = pd.DataFrame(obgyn_df['regionabbr'].value_counts().reset_index().values, columns=['regionabbr','Provider_Count'])

# Plot OB-GYN Providers by State

plt.figure(figsize=(25,12))
sns.barplot(obgyn_count_df['regionabbr'].iloc[:-8], obgyn_count_df['Provider_Count'].iloc[:-8])
plt.title('OB-GYN Providers by State', fontsize=22)
plt.xlabel('States', fontsize=18)
plt.ylabel('Provider Count', fontsize=18)

Text(0, 0.5, 'Provider Count')

Merge Provider Count and Population Dataframes¶

# Merge provider count and women_df at state level
state_obgyn_df = pd.merge(obgyn_count_df,fertility_df,left_on='regionabbr', right_on='STUSPS',how='inner')

# Create new columns that shows provider by women pop
state_obgyn_df['women_per_prov'] = state_obgyn_df['B13016_001E']/state_obgyn_df['Provider_Count']
# Create new columns that shows provider by mother pop
state_obgyn_df['mother_per_prov'] = state_obgyn_df['B13016_002E']/state_obgyn_df['Provider_Count']

# Arrange dataframe by mother_per_prov descending
state_obgyn_df = state_obgyn_df.sort_values(by=['mother_per_prov'], ascending=False)
state_obgyn_df

# Plot No. of Mothers (age 15 to 50) per OB-GYN Provider by State

plt.figure(figsize=(25,12))
sns.barplot(state_obgyn_df['regionabbr'], state_obgyn_df['mother_per_prov'])
plt.title('No. of Mothers (age 15 to 50) per OB-GYN Provider by State', fontsize=22)
plt.xlabel('States', fontsize=18)
plt.ylabel('No. of Mothers (age 15 to 50)', fontsize=18)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)

(array([  0.,  20.,  40.,  60.,  80., 100., 120.]),
 <a list of 7 Text yticklabel objects>)

On average, there were ~112 mothers and 1655 women per provider in ND compared to ~31 mothers and 662 women per provider in DC. The difference is drastic.

Exploring North Dakota¶

North Dakota has the highest number of mothers and women per OB-GYN provider. From the fertility map above, we also saw that percent of women (15 to 50) who had a birth in the past 12 months is high in North Dakota.

Let's explore North Dakota to find out which counties have the lowest number of OBGYN providers.

Dataframe for women population in North Dakota¶

# State population df
ND_featureset = fertility_county.query(where="STATE='North Dakota'")
ND_fertility_df = ND_featureset.sdf
ND_fertility_df.head()

Dataframe for OB-GYN providers in North Dakota by County¶

# Get provider data for obgyn providers only

obgyn_ND_featureset = provider_data_layer.query(where="Region='North Dakota' AND (user_taxonomy_code_1 in ('207V00000X','207VC0200X','207VF0040X','207VX0201X','207VG0400X','207VH0002X','207VM0101X','207VB0002X','207VX0000X','207VE0102X','363LX0001X'))") 
#                           out_fields='user_npi,user_entity_type,user_provider_gender,user_taxonomy_code_1,user_full_address,Postal,City,Subregion,Region,RegionAbbr')
obgyn_ND_df = obgyn_ND_featureset.sdf
obgyn_ND_df.head()

# Create dataframe of obgyn provider counts by county
obgyn_NDcounty_df = pd.DataFrame(obgyn_ND_df['subregion'].value_counts().reset_index().values, columns=['County','Provider_Count'])
obgyn_NDcounty_df.head()

Merge Provider Count and Population Dataframes¶

# Merge provider count and women data at county level for ND
county_obgyn_df = pd.merge(ND_fertility_df,obgyn_NDcounty_df,left_on='NAME', right_on='County',how='left')

# Create new columns that shows provider by women pop
county_obgyn_df['women_per_prov'] = county_obgyn_df['B13016_001E']/county_obgyn_df['Provider_Count']
# Create new columns that shows provider by mother pop
county_obgyn_df['mother_per_prov'] = county_obgyn_df['B13016_002E']/county_obgyn_df['Provider_Count']

# Arrange dataframe by mother_per_prov descending and then B13016_002E (women who gave birht) descending 
county_obgyn_df = county_obgyn_df.sort_values(by=['mother_per_prov','B13016_002E'], ascending=False)[['NAME','Provider_Count','B13016_001E','B13016_002E','women_per_prov','mother_per_prov']]
county_obgyn_df.columns = ['Name','Provider_Count', "Total Women (15 to 50)","Women who had birth (past 12 months)", 'women_per_prov', 'mother_per_prov']
county_obgyn_df

43 out of 53 counties in ND do not have any Obgyn healthcare providers.
Morton County seems to be the worst with 463 women who gave birth but no provider.

Summarize Data - GA Tools¶

# Create provider count df
from arcgis.geoanalytics import summarize_data
fertility_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/0")
sum_fields = ['user_npi Count']
eq_summary = summarize_data.aggregate_points(point_layer = provider_data_layer.filter("OBJECTID < 10"),
                                            polygon_layer = fertility_state,
                                             output_name='test_women_agg')
#                                             group_by_field = 'STUSPS',
#                                             summary_fields=sum_fields)

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-100-ad333b42341e> in <module>
      3 fertility_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/0")
      4 sum_fields = ['user_npi Count']
----> 5 eq_summary = summarize_data.aggregate_points(point_layer = provider_data_layer.filter("OBJECTID < 10"),
      6                                             polygon_layer = fertility_state,
      7                                              output_name='test_women_agg')

TypeError: 'NoneType' object is not callable

# Create provider count df
from arcgis.features import summarize_data
fertility_state = FeatureLayer("https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/ACS_Fertility_by_Age_Boundaries/FeatureServer/0")
sum_fields = ['user_npi Count']
eq_summary = summarize_data.aggregate_points(point_layer = provider_data_layer,
                                            polygon_layer = fertility_state,
                                            keep_boundaries_with_no_points=False,
                                            group_by_field = 'STUSPS',
                                            summary_fields = sum_fields)

GetLayers for parameter 0 failed. Error: {"code" : 0, "messageCode":"GPEXT_018","message": "Number of features in service https://datascienceqa.esri.com/server/rest/services/Hosted/provider_clean_data_geocoded_6_19/FeatureServer/0 exceeds the limit of 100,000 features. Use a hosted feature service as input to analyze large dataset.","params":{"url" : "https://datascienceqa.esri.com/server/rest/services/Hosted/provider_clean_data_geocoded_6_19/FeatureServer/0"}}
{"messageCode": "AO_100001", "message": "AggregatePoints failed."}
Failed to execute (AggregatePoints).
Failed.

---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-11-860b6902dbc3> in <module>
      7                                             keep_boundaries_with_no_points=False,
      8                                             group_by_field = 'STUSPS',
----> 9                                             summary_fields=sum_fields)

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\features\summarize_data.py in aggregate_points(point_layer, polygon_layer, keep_boundaries_with_no_points, summary_fields, group_by_field, minority_majority, percent_points, output_name, context, gis, estimate)
     95                      output_name,
     96                      context,
---> 97                      estimate=estimate)
     98 
     99 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\tools.py in aggregate_points(self, point_layer, polygon_layer, keep_boundaries_with_no_points, summary_fields, group_by_field, minority_majority, percent_points, output_name, context, estimate)
    504         task_url, job_info, job_id = super()._analysis_job(task, params)
    505 
--> 506         job_info = super()._analysis_job_status(task_url, job_info)
    507         job_values = super()._analysis_job_results(task_url, job_info, job_id)
    508         #print(job_values)

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\tools.py in _analysis_job_status(self, task_url, job_info)
    187 
    188                     if job_response.get("jobStatus") == "esriJobFailed":
--> 189                         raise Exception("Job failed.")
    190                     elif job_response.get("jobStatus") == "esriJobCancelled":
    191                         raise Exception("Job cancelled.")

Exception: Job failed.

import arcpy

# Set local variables
# This example used a big data file share name "Crimes" with dataset "Chicago" registered on my GeoAnalytics server
inFeatures = "https://MyGeoAnalyticsMachine.domain.com/geoanalytics/rest/services/DataStoreCatalogs/bigDataFileShares_Crimes/BigDataCatalogServer/Chicago"
summaryFields = "Region"
# summaryStatistics = [["user_npi", "COUNT"]]
outFS = 'SummarizeStates'


# Execute SummarizeAttributes
arcpy.geoanalytics.SummarizeAttributes(provider_data_layer, outFS, summaryFields)

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-9-85d7a01ac9d5> in <module>
     10 
     11 # Execute SummarizeAttributes
---> 12 arcpy.geoanalytics.SummarizeAttributes(provider_data_layer, outFS, summaryFields)

~\AppData\Local\Programs\ArcGIS\Pro\Resources\ArcPy\arcpy\geoanalytics.py in SummarizeAttributes(input_layer, output_name, fields, summary_fields, data_store)
   1732         return retval
   1733     except Exception as e:
-> 1734         raise e
   1735 
   1736 @gptooldoc('SummarizeWithin_geoanalytics', None)

~\AppData\Local\Programs\ArcGIS\Pro\Resources\ArcPy\arcpy\geoanalytics.py in SummarizeAttributes(input_layer, output_name, fields, summary_fields, data_store)
   1729     from arcpy.arcobjects.arcobjectconversion import convertArcObjectToPythonObject
   1730     try:
-> 1731         retval = convertArcObjectToPythonObject(gp.SummarizeAttributes_geoanalytics(*gp_fixargs((input_layer, output_name, fields, summary_fields, data_store), True)))
   1732         return retval
   1733     except Exception as e:

~\AppData\Local\Programs\ArcGIS\Pro\Resources\ArcPy\arcpy\geoprocessing\_base.py in <lambda>(*args)
    504         val = getattr(self._gp, attr)
    505         if callable(val):
--> 506             return lambda *args: val(*gp_fixargs(args, True))
    507         else:
    508             return convertArcObjectToPythonObject(val)

RuntimeError: Object: Error in executing tool

Clustering¶

The idea of clustering is to find out if observations in the data naturally group together in some predictable way. In our case, we want to know if there is natural grouping that distinguishes some states from others. Standardization - most cluster analysis algorithms depend on the concept of measuring the distance between the different observations we're trying to cluster. If one of the variables is measured on a much larger scale than the other variables, then whatever measure we use will be overly influenced by the variable that has larger scale. So, we standardize our data to bring all variables to the same scale.

OB-GYN Providers and County Data¶

Create dataframe for OBGYN Providers by County¶

# Get provider data for obgyn providers only
obgyn_df.head()

obgyn_df.shape

(66754, 12)

obgyn_county_df = obgyn_df[['regionabbr','subregion']]
obgyn_county_df['Provider_Count'] = obgyn_county_df.groupby(['regionabbr','subregion'])['subregion'].transform('count')
obgyn_count_df = obgyn_county_df.drop_duplicates(subset='subregion', keep="first", inplace=True)
obgyn_county_df.shape

C:\Users\mohi9282\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\ipykernel_launcher.py:2: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  
C:\Users\mohi9282\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\ipykernel_launcher.py:3: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  This is separate from the ipykernel package so we can avoid doing imports until

(1282, 3)

obgyn_county_df = obgyn_county_df.sort_values(by=['Provider_Count'], ascending=False)
obgyn_county_df.head()

# Check missing
obgyn_county_df.isnull().sum()

regionabbr        0
subregion         0
Provider_Count    0
dtype: int64

Create dataframe for County Data¶

county_layer = popdensity.layers[46]
county_df = pd.DataFrame()
offset = 0
while offset <= 3000:
    county_featureset_test = county_layer.query(return_all_records=False,
                                           result_offset=offset,
                                           result_record_count=750)
    county_df_test = county_featureset_test.sdf
    county_df = pd.concat([county_df_test, county_df], ignore_index=True)
    offset += 750

county_df.shape

(3142, 1943)

# Removing columns for 2023,2010,2000
county_df = county_df.loc[:,~county_df.columns.str.contains('FY|10|00')]
len(county_df.columns)

894

# Removing columns for individual age
county_df = county_df.loc[:,~county_df.columns.str.startswith(('AGE','MAGE','FAGE'))]
len(county_df.columns)

640

# Removing columns for Industry, Occupation
county_df = county_df.loc[:,~county_df.columns.str.startswith(('IND','OCC'))]
len(county_df.columns)

596

# Removing Individual Income Columns
county_df = county_df.loc[:,~county_df.columns.str.contains('A15|A25|A35|A45|A55|A65|A75')]
len(county_df.columns)

366

# Removing columns for Disposable Income and Net Worth
county_df = county_df.loc[:,~county_df.columns.str.startswith(('DI','NW'))]
len(county_df.columns)

348

# Removing columns for Tapestry Segmentation
county_df = county_df.loc[:,~county_df.columns.str.startswith(('TSE','THH','TADULT'))]
len(county_df.columns)

190

# Removing columns for Home Value
county_df = county_df.loc[:,~county_df.columns.str.startswith(('VAL'))]
len(county_df.columns)

181

county_df.columns

Index(['AAGEBASECY', 'AGGDI_CY', 'AGGHINC_CY', 'AGGINC_CY', 'AGGNW_CY',
       'AIFBASE_CY', 'AIMBASE_CY', 'AMERIND_CY', 'AREA', 'ASIAN_CY',
       ...
       'TOTHU_CY', 'TOTPOP_CY', 'UNEMPRT_CY', 'UNEMP_CY', 'VACANT_CY',
       'WAGEBASECY', 'WHITE_CY', 'WHTFBASECY', 'WHTMBASECY', 'WIDOWED_CY'],
      dtype='object', length=181)

print(obgyn_county_df.shape)
print(county_df.shape)

(1282, 3)
(3142, 181)

Merge dataframes¶

# Merge provider count and women_df at state level
newcounty_obgyn_df = pd.merge(obgyn_county_df,county_df,left_on=['regionabbr','subregion'], right_on=['ST_ABBREV','NAME'],how='left')

newcounty_obgyn_df.shape

(1282, 184)

# Create a copy
test_newcounty_df = newcounty_obgyn_df.copy()
test_newcounty_df.head()

test_newcounty_df.drop(['OBJECTID','SHAPE','STATE_NAME','ST_ABBREV','NAME','Shape_Area','Shape_Length','TLIFENAME','ID','TLIFECODE'], axis=1, inplace=True)
test_newcounty_df.head()

# Change Provider Count to Float
test_newcounty_df['Provider_Count'] = test_newcounty_df['Provider_Count'].astype(float)

test_newcounty_df.head()

Handle Missing Values¶

# Any missing values in the data
test_newcounty_df.isnull().sum().sum()

21375

# Columns with missing values
test_newcounty_df.isnull().sum()

regionabbr          0
subregion           0
Provider_Count      0
AAGEBASECY        125
AGGDI_CY          125
AGGHINC_CY        125
AGGINC_CY         125
AGGNW_CY          125
AIFBASE_CY        125
AIMBASE_CY        125
AMERIND_CY        125
AREA              125
ASIAN_CY          125
ASNFBASECY        125
ASNMBASECY        125
ASSCDEG_CY        125
AVGDI_CY          125
AVGFMSZ_CY        125
AVGHHSZ_CY        125
AVGHINC_CY        125
AVGNW_CY          125
AVGVAL_CY         125
BABYBOOMCY        125
BACHDEG_CY        125
BAGEBASECY        125
BLACK_CY          125
BLKFBASECY        125
BLKMBASECY        125
CIVLBFR_CY        125
EDUCBASECY        125
                 ... 
POP30_CY          125
POP35_CY          125
POP40_CY          125
POP45_CY          125
POP50_CY          125
POP55_CY          125
POP5_CY           125
POP60_CY          125
POP65_CY          125
POP70_CY          125
POP75_CY          125
POP80_CY          125
POP85_CY          125
POPDENS_CY        125
RACE2UP_CY        125
RACEBASECY        125
RENTER_CY         125
SMCOLL_CY         125
SOMEHS_CY         125
TOTHH_CY          125
TOTHU_CY          125
TOTPOP_CY         125
UNEMPRT_CY        125
UNEMP_CY          125
VACANT_CY         125
WAGEBASECY        125
WHITE_CY          125
WHTFBASECY        125
WHTMBASECY        125
WIDOWED_CY        125
Length: 174, dtype: int64

# Dataframe of rows and columns with null
null_data = test_newcounty_df[test_newcounty_df.isnull().any(axis=1)]
null_data.head()

null_data.shape

(125, 174)

# Rows where all columns (except region, subregion, Provider Count) are null
test_newcounty_df.index[test_newcounty_df.iloc[:,3:].isnull().all(1)]

Int64Index([  29,   30,   35,   39,   50,   70,   78,  128,  132,  157,
            ...
            1244, 1245, 1251, 1254, 1259, 1266, 1268, 1271, 1278, 1280],
           dtype='int64', length=125)

test_newcounty_df.dropna(inplace=True)

test_newcounty_df.isnull().sum().sum()

0

# Check if any values are 0 in the dataframe
# test_newstate_df.isnull().values.any()
test_newcounty_df.columns[test_newcounty_df.eq(0).any()].tolist()

['GQPOP_CY',
 'HISPAI_CY',
 'HISPASN_CY',
 'HISPBLK_CY',
 'HISPPI_CY',
 'NHSPOTH_CY',
 'NHSPPI_CY',
 'OTHMBASECY',
 'PACIFIC_CY',
 'PAGEBASECY',
 'PIFBASE_CY',
 'PIMBASE_CY']

test_newcounty_df.drop(['GQPOP_CY','HISPAI_CY','HISPASN_CY','HISPBLK_CY','HISPPI_CY','NHSPOTH_CY','NHSPPI_CY','OTHMBASECY','PACIFIC_CY','PAGEBASECY','PIFBASE_CY','PIMBASE_CY'], axis=1, inplace=True)

test_newcounty_df.head()

test_newcounty_df.columns[test_newcounty_df.eq(0).any()].tolist()

[]

test_newcounty_df['MEDHINC_CY'] = test_newcounty_df['MEDHINC_CY'].astype(float)
type(test_newcounty_df['MEDHINC_CY'][0])

numpy.float64

k-means¶

Determine optimal clusters using within cluster SSE¶

# Transform Data
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import PowerTransformer
# pt_county = PowerTransformer(method='box-cox')
sc_county = StandardScaler()
newcounty_df_pt = sc_county.fit_transform(test_newcounty_df.iloc[:,2:])

from sklearn.cluster import KMeans
sns.set(rc={'figure.figsize':(15,8)})
distortions = []
for i in range(1,20):
    km = KMeans(n_clusters=i,
                init='k-means++',
                n_init=10,
                max_iter=300,
                random_state=0)
    km.fit(newcounty_df_pt)
    distortions.append(km.inertia_)
    
plt.plot(range(1,20), distortions, marker='o')
plt.xlabel('No. of Clusters')
plt.ylabel('Within Cluster Sum of Squares')
plt.show()

Run KMeans with 4 clusters¶

sns.set(rc={'figure.figsize':(15,8)})
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=4)
# Fit the data
kmeans.fit(newcounty_df_pt)
# Get the cluster labels
y_pred = kmeans.predict(newcounty_df_pt)
# plot the cluster assignments and cluster centers
# newstate_df_std[:,0] includes all values for Provider Count and so on... #103 is MEDAGE_CY
plt.scatter(newcounty_df_pt[:, 0], newcounty_df_pt[:,103], c=y_pred, cmap="plasma")
plt.scatter(kmeans.cluster_centers_[:, 0],   
            kmeans.cluster_centers_[:, 103],
            marker='^', 
            c=[0,1,2,3], 
            s=100, 
            linewidth=2,
            cmap="plasma")
plt.xlabel("Feature 0")
plt.ylabel("Feature 1")

Text(0, 0.5, 'Feature 1')

kmeans.cluster_centers_

array([[-2.29317364e-01, -1.64233279e-01, -2.43219407e-01,
        -2.42675763e-01, -2.42411870e-01, -2.58458545e-01,
        -1.25736786e-01, -1.29842473e-01, -1.27814032e-01,
        -6.32433407e-03, -1.64233279e-01, -1.63502697e-01,
        -1.64971132e-01, -2.43841359e-01, -1.71135994e-01,
        -1.09379498e-01, -7.08485772e-02, -1.78882907e-01,
        -1.20255165e-01, -1.61501958e-01, -2.42271740e-01,
        -2.39829177e-01, -2.01811386e-01, -2.01811386e-01,
        -1.99028814e-01, -2.04901056e-01, -2.35039704e-01,
        -2.34328504e-01, -2.35124412e-01, -2.39545162e-01,
        -2.30936558e-01, -2.27053923e-01, -2.30186600e-01,
        -2.34011637e-01, -2.21017499e-01, -2.34349625e-01,
        -2.19176247e-01, -2.22922791e-01, -2.28501614e-01,
        -2.31508456e-01, -2.33838305e-01, -2.37733664e-01,
        -2.41251929e-01, -2.31321995e-01, -2.42318230e-01,
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        -2.47446180e-01, -2.25527482e-01, -2.32809623e-01,
        -2.32589872e-01, -2.30871750e-01, -2.43967347e-01,
        -1.59092857e-01, -2.32628181e-01, -2.16968477e-01,
        -2.44474646e-01, -2.23260653e-01, -2.30636951e-01,
        -2.34340471e-01, -2.38507791e-01, -2.45285761e-01,
        -2.41071102e-01, -1.76294818e-01, -1.45324887e-01,
        -1.59092857e-01, -1.53467221e-01, -2.34326215e-01,
        -1.58466181e-01, -1.59661881e-01, -1.27814032e-01,
        -6.36804859e-03, -2.31849228e-01, -2.32207043e-01,
        -2.26959236e-01, -2.28578630e-01, -2.18881238e-01,
        -2.18532889e-01, -2.20635024e-01, -2.25261640e-01,
        -2.29207308e-01, -2.32103140e-01, -2.36036632e-01,
        -2.40570229e-01, -2.30933444e-01, -2.42256405e-01,
        -2.44170012e-01, -2.43300353e-01, -2.40061138e-01,
        -2.38785309e-01, -2.36920079e-01, -2.31823618e-01,
        -2.32954494e-01, -2.38701971e-01,  4.66452208e-02,
        -1.51163422e-01,  4.59806691e-02,  6.92172601e-02,
        -1.49514500e-01,  4.82673939e-02, -8.00094652e-02,
        -1.59732505e-01, -2.21450216e-01, -1.91147282e-01,
        -2.12216304e-01, -2.10444754e-01, -2.18099725e-01,
        -7.99587097e-02, -1.64159154e-01, -1.99943896e-01,
        -2.29677993e-01, -2.68676366e-01, -1.58374936e-01,
        -2.58037228e-01, -1.46986206e-01, -2.41282616e-01,
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        -2.33001578e-01, -2.20032660e-01, -2.33352807e-01,
        -2.18896430e-01, -2.21811229e-01, -2.26915755e-01,
        -2.30404339e-01, -2.33013688e-01, -2.36933736e-01,
        -2.40961563e-01, -2.31125896e-01, -2.42334198e-01,
        -2.43582614e-01, -2.42636978e-01, -2.40297932e-01,
        -2.40132653e-01, -2.41402648e-01, -1.10083858e-01,
        -2.11376777e-01, -2.32809623e-01, -2.11037184e-01,
        -2.32599146e-01, -1.97609564e-01, -2.41070897e-01,
        -2.41160205e-01, -2.32809623e-01, -9.25778130e-04,
        -2.26430209e-01, -2.13653291e-01, -2.49746142e-01,
        -2.49746142e-01, -2.51066185e-01, -2.48346659e-01,
        -2.35912390e-01],
       [ 4.80078360e+00,  4.05313502e+00,  4.96050868e+00,
         4.92235367e+00,  4.92231503e+00,  4.42351752e+00,
         3.01412455e+00,  3.08982775e+00,  3.05279888e+00,
         7.91286005e-01,  4.05313502e+00,  4.02397084e+00,
         4.08361504e+00,  4.97095309e+00,  1.13930456e+00,
         1.48409904e+00,  9.48205317e-01,  1.20786161e+00,
         1.37658143e-01,  1.94037786e+00,  4.90037605e+00,
         4.82721802e+00,  4.52443696e+00,  4.52443696e+00,
         4.47652784e+00,  4.57676180e+00,  4.93072654e+00,
         4.95987578e+00,  4.91430101e+00,  4.98155721e+00,
         4.92847786e+00,  5.01871898e+00,  4.83205338e+00,
         4.94921046e+00,  4.82935806e+00,  4.95774237e+00,
         5.02079667e+00,  5.03509687e+00,  5.01708829e+00,
         4.98059210e+00,  4.91921200e+00,  4.87664896e+00,
         4.88265879e+00,  4.99709477e+00,  4.90522364e+00,
         4.92157941e+00,  4.90043702e+00,  4.84794858e+00,
         4.76631608e+00,  4.63332209e+00,  4.96209509e+00,
         5.45100522e+00,  5.02436112e+00,  4.95760606e+00,
         4.94241937e+00,  4.93833747e+00,  4.88810006e+00,
         4.36902434e+00,  4.96333273e+00,  4.87351440e+00,
         4.74859359e+00,  4.87610819e+00,  5.02987544e+00,
         5.03084203e+00,  5.02639892e+00,  5.01627438e+00,
         5.03379061e+00,  4.34797747e+00,  3.72600120e+00,
         4.36902434e+00,  4.49838089e+00,  4.87897419e+00,
         4.36501350e+00,  4.37142110e+00,  3.05279888e+00,
         7.99255101e-01,  4.94071160e+00,  4.94874408e+00,
         5.01360982e+00,  4.82137327e+00,  4.79916115e+00,
         4.99411997e+00,  5.01408540e+00,  4.99415068e+00,
         4.97131179e+00,  4.92444096e+00,  4.88273133e+00,
         4.87778884e+00,  4.98583071e+00,  4.88554413e+00,
         4.90280643e+00,  4.88878508e+00,  4.83792822e+00,
         4.73554875e+00,  4.57709902e+00,  4.95180809e+00,
         4.94277883e+00,  4.96899600e+00, -7.09243260e-01,
         9.16802203e-01, -7.20309943e-01, -9.29561859e-01,
         8.17387185e-01, -6.97657487e-01, -2.84475846e-01,
         1.94066936e+00,  4.96903290e+00,  4.83081361e+00,
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         1.83493927e+00,  4.05172448e+00,  4.46547213e+00,
         4.64667349e+00,  4.46985252e+00,  4.04283823e+00,
         4.88162711e+00,  3.75522902e+00,  4.78142782e+00,
         3.73672192e+00,  3.75522902e+00,  4.89029611e+00,
         9.51091475e-01,  5.01615518e+00,  4.82711959e+00,
         4.94592142e+00,  4.81617528e+00,  4.95423889e+00,
         5.00834727e+00,  5.02543350e+00,  5.00648423e+00,
         4.97676747e+00,  4.92250022e+00,  4.88031515e+00,
         4.88108246e+00,  4.99139497e+00,  4.89686185e+00,
         4.91411043e+00,  4.89690982e+00,  4.84571131e+00,
         4.75674826e+00,  4.61894258e+00,  3.03313609e+00,
         4.64981399e+00,  4.95760606e+00,  4.95506435e+00,
         4.92450370e+00,  4.81818724e+00,  5.03378089e+00,
         5.08311303e+00,  4.95760606e+00,  2.45689242e-01,
         5.08008604e+00,  4.98874827e+00,  4.91858407e+00,
         4.91858407e+00,  4.91349190e+00,  4.92275350e+00,
         4.83857696e+00],
       [ 1.67155862e+01,  2.25017391e+01,  1.82355479e+01,
         1.81634556e+01,  1.82048849e+01,  1.54887938e+01,
         1.09780613e+01,  1.20059274e+01,  1.14871195e+01,
         1.24592475e+00,  2.25017391e+01,  2.27937310e+01,
         2.21707257e+01,  1.78202715e+01,  1.22454020e+00,
         2.62404528e+00,  2.01345162e+00,  1.29423189e+00,
         5.15097380e-02,  3.59070605e+00,  1.88707660e+01,
         1.85001059e+01,  9.52587163e+00,  9.52587163e+00,
         9.35163714e+00,  9.72191876e+00,  1.95124195e+01,
         1.95607998e+01,  1.96286414e+01,  1.87632756e+01,
         1.98101980e+01,  1.90537962e+01,  1.99868122e+01,
         1.95818945e+01,  2.04642611e+01,  1.95318088e+01,
         2.02046032e+01,  1.99742698e+01,  1.96755275e+01,
         1.96898775e+01,  1.98455034e+01,  1.96329742e+01,
         1.91321957e+01,  1.89143994e+01,  1.87850701e+01,
         1.84555995e+01,  1.83570224e+01,  1.83629915e+01,
         1.80478946e+01,  1.75499070e+01,  1.95241910e+01,
         1.23242663e+01,  1.91249716e+01,  1.96244578e+01,
         1.98475196e+01,  1.93745517e+01,  1.60186536e+01,
         2.39118269e+01,  1.96221773e+01,  1.79167059e+01,
         1.76460726e+01,  1.86261285e+01,  1.75381284e+01,
         1.75613734e+01,  1.78828441e+01,  1.75569008e+01,
         1.81973716e+01,  2.39463529e+01,  2.64631680e+01,
         2.39118269e+01,  2.06028030e+01,  1.86027591e+01,
         2.38906796e+01,  2.39241384e+01,  1.14871195e+01,
         1.26017972e+00,  1.98210134e+01,  1.97781317e+01,
         1.91169007e+01,  2.01531888e+01,  2.06388334e+01,
         2.03290953e+01,  2.01543956e+01,  1.99674684e+01,
         1.98953526e+01,  2.00099612e+01,  1.98430700e+01,
         1.92982694e+01,  1.90228065e+01,  1.89066199e+01,
         1.83958068e+01,  1.82318225e+01,  1.84264017e+01,
         1.83006259e+01,  1.83230097e+01,  1.97237523e+01,
         1.97170930e+01,  1.89579112e+01, -8.50146499e-01,
         8.90420073e-01, -8.45998824e-01, -8.89347617e-01,
         6.77475274e-01, -8.39798851e-01, -7.39841991e-01,
         3.34823689e+00,  2.02528974e+01,  2.23864877e+01,
         2.15556298e+01,  2.15726551e+01,  2.11310606e+01,
         3.52837169e+00,  2.24384965e+01,  9.38158416e+00,
         1.82896494e+01,  1.23216349e+01,  2.59655994e+01,
         1.51882793e+01,  2.64096155e+01,  1.82146407e+01,
         2.64962908e+01,  2.64096155e+01,  1.53228599e+01,
         5.48135978e-01,  1.90861038e+01,  2.00744134e+01,
         1.97012668e+01,  2.05614215e+01,  1.96543955e+01,
         2.02717859e+01,  2.00686397e+01,  1.98258036e+01,
         1.97947319e+01,  1.99296598e+01,  1.97388514e+01,
         1.92152144e+01,  1.89698398e+01,  1.88458527e+01,
         1.84324735e+01,  1.83070597e+01,  1.83988591e+01,
         1.81662124e+01,  1.78544947e+01,  7.32630057e-01,
         2.15672794e+01,  1.96244578e+01,  2.05241422e+01,
         1.87125765e+01,  2.20615458e+01,  1.81974760e+01,
         1.77596149e+01,  1.96244578e+01, -1.57011303e-01,
         1.68183164e+01,  1.11872632e+01,  1.67788181e+01,
         1.67788181e+01,  1.65988761e+01,  1.69589683e+01,
         1.87377110e+01],
       [ 1.05388157e+00,  5.79175679e-01,  1.13286987e+00,
         1.13455348e+00,  1.13204465e+00,  1.35953914e+00,
         5.07240115e-01,  5.21685231e-01,  5.14583282e-01,
        -7.37388110e-02,  5.79175679e-01,  5.75040906e-01,
         5.83495145e-01,  1.13985002e+00,  1.23553668e+00,
         6.64084250e-01,  4.29513484e-01,  1.28886512e+00,
         9.71785664e-01,  1.01956894e+00,  1.12868522e+00,
         1.12224865e+00,  9.25795287e-01,  9.25795287e-01,
         9.11261038e-01,  9.42040168e-01,  1.05934799e+00,
         1.04882006e+00,  1.06150585e+00,  1.09517914e+00,
         1.02339681e+00,  9.84300766e-01,  1.02988313e+00,
         1.04759455e+00,  9.50732154e-01,  1.04954226e+00,
         9.09633716e-01,  9.40330238e-01,  9.91431072e-01,
         1.02146787e+00,  1.04841106e+00,  1.08851231e+00,
         1.12073703e+00,  1.02381886e+00,  1.12905616e+00,
         1.13475343e+00,  1.13009851e+00,  1.12415694e+00,
         1.14297209e+00,  1.18836061e+00,  1.04368560e+00,
         1.14402856e+00,  9.70300181e-01,  1.03610082e+00,
         1.03469636e+00,  1.02496098e+00,  1.16766181e+00,
         4.79064914e-01,  1.03378404e+00,  9.31569422e-01,
         1.17903259e+00,  9.77345419e-01,  1.02455788e+00,
         1.05478901e+00,  1.08721450e+00,  1.14727811e+00,
         1.10472817e+00,  6.23815392e-01,  4.38809521e-01,
         4.79064914e-01,  4.40614395e-01,  1.06842938e+00,
         4.74652448e-01,  4.83309680e-01,  5.14583282e-01,
        -7.46601807e-02,  1.02904995e+00,  1.03117580e+00,
         9.83754046e-01,  1.01682441e+00,  9.36103430e-01,
         9.07216489e-01,  9.23061922e-01,  9.65677915e-01,
         1.00216628e+00,  1.03199016e+00,  1.07191014e+00,
         1.11447409e+00,  1.02137956e+00,  1.13043770e+00,
         1.14785561e+00,  1.14406426e+00,  1.12319468e+00,
         1.12867142e+00,  1.13628527e+00,  1.02800548e+00,
         1.03871301e+00,  1.08847692e+00, -2.74214423e-01,
         1.10600011e+00, -2.67144726e-01, -4.27919170e-01,
         1.10867295e+00, -2.89380254e-01,  7.07886296e-01,
         1.00689599e+00,  9.35581133e-01,  6.88401291e-01,
         8.94552723e-01,  8.81943335e-01,  9.28798554e-01,
         3.62608225e-01,  5.79284587e-01,  9.20186762e-01,
         1.06661293e+00,  1.46282546e+00,  5.04177825e-01,
         1.29146520e+00,  4.48676925e-01,  1.14326434e+00,
         4.40643704e-01,  4.48676925e-01,  1.28793030e+00,
         1.16371172e+00,  9.84029960e-01,  1.02330043e+00,
         1.03877032e+00,  9.43744497e-01,  1.04083255e+00,
         9.08600320e-01,  9.31804507e-01,  9.78674570e-01,
         1.01206388e+00,  1.04044089e+00,  1.08051396e+00,
         1.11789665e+00,  1.02258402e+00,  1.12991744e+00,
         1.14105604e+00,  1.13678940e+00,  1.12423365e+00,
         1.13778017e+00,  1.17096119e+00,  4.58585659e-01,
         8.88483719e-01,  1.03610082e+00,  8.49491389e-01,
         1.04662187e+00,  7.46217812e-01,  1.10472706e+00,
         1.10180454e+00,  1.03610082e+00, -2.70384574e-02,
         9.88347940e-01,  9.42059826e-01,  1.20470734e+00,
         1.20470734e+00,  1.21780852e+00,  1.19108396e+00,
         1.08633317e+00]])

Identify which counties belong to what cluster¶

# Create dataframe of Cluster labels and State Names
clusterdf = pd.DataFrame(kmeans.labels_, test_newcounty_df['subregion'])
clusterdf.reset_index(inplace=True)
clusterdf.columns = ['County','Cluster']
clusterdf.head()

# Count of counties in each cluster
clusterdf['Cluster'].value_counts()

0    1015
3     123
1      18
2       1
Name: Cluster, dtype: int64

# Counties in each cluster
clusterdf.groupby('Cluster')['County'].apply(list)

Cluster
0    [Lehigh County, Washtenaw County, Pulaski Coun...
1    [Cook County, New York County, Harris County, ...
2                                 [Los Angeles County]
3    [Philadelphia County, Oakland County, Fulton C...
Name: County, dtype: object

Test 2 - OBGYN Providers¶

DF for OBGYN Providers by State¶

# Get provider data for obgyn providers only

obgyn_count_df.head()

Create df for Data from State Layer¶

allstate_featureset = state_layer.query()
allstate_df = allstate_featureset.sdf
allstate_df.head()

Error invoking service
Error generating token

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-144-a9c8dabe6cbf> in <module>
----> 1 allstate_featureset = state_layer.query()
      2 allstate_df = allstate_featureset.sdf
      3 allstate_df.head()

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\features\layer.py in query(self, where, out_fields, time_filter, geometry_filter, return_geometry, return_count_only, return_ids_only, return_distinct_values, return_extent_only, group_by_fields_for_statistics, statistic_filter, result_offset, result_record_count, object_ids, distance, units, max_allowable_offset, out_sr, geometry_precision, gdb_version, order_by_fields, out_statistics, return_z, return_m, multipatch_option, quantization_parameters, return_centroid, return_all_records, result_type, historic_moment, sql_format, return_true_curves, return_exceeded_limit_features, as_df, **kwargs)
    698 
    699         params['returnCountOnly'] = True
--> 700         record_count = self._query(url, params, raw=as_raw)
    701         if 'maxRecordCount' in self.properties:
    702             max_records = self.properties['maxRecordCount']

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\features\layer.py in _query(self, url, params, raw)
   1397         """ returns results of query """
   1398         result = self._con.post(path=url,
-> 1399                                 postdata=params, token=self._token)
   1400         if 'error' in result:
   1401             raise ValueError(result)

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\connection.py in post(self, path, postdata, files, ssl, compress, is_retry, use_ordered_dict, add_token, verify_cert, token, try_json, out_folder, file_name, force_bytes, add_headers)
   1175                                          verify_cert=verify_cert, is_retry=True)
   1176 
-> 1177                 self._handle_json_error(resp_json['error'], errorcode)
   1178                 return None
   1179 

~\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\arcgis\_impl\connection.py in _handle_json_error(self, error, errorcode)
   1196 
   1197         errormessage = errormessage + "\n(Error Code: " + str(errorcode) +")"
-> 1198         raise RuntimeError(errormessage)
   1199 
   1200 class _StrictURLopener(request.FancyURLopener):

RuntimeError: Error invoking service
Error generating token
(Error Code: 500)

for field in state_layer.properties.fields:
#     print(field)
    print(field['name'], '\t', field['alias'])
#     print(field[0])

OBJECTID 	 OBJECTID
Shape 	 Shape
ID 	 ID
NAME 	 NAME
STATE_NAME 	 STATE_NAME
ST_ABBREV 	 ST_ABBREV
AREA 	 Area in Square Miles (Calculated)
TOTPOP_CY 	 2018 Total Population (Esri)
HHPOP_CY 	 2018 Household Population (Esri)
FAMPOP_CY 	 2018 Family Population (Esri)
GQPOP_CY 	 2018 Group Quarters Population (Esri)
POPDENS_CY 	 2018 Population Density (Pop per Square Mile) (Esri)
TOTHH_CY 	 2018 Total Households (Esri)
AVGHHSZ_CY 	 2018 Average Household Size (Esri)
FAMHH_CY 	 2018 Total Family Households (Esri)
AVGFMSZ_CY 	 2018 Average Family Size (Esri)
TOTHU_CY 	 2018 Total Housing Units (Esri)
OWNER_CY 	 2018 Owner Occupied Housing Units (Esri)
RENTER_CY 	 2018 Renter Occupied Housing Units (Esri)
VACANT_CY 	 2018 Vacant Housing Units (Esri)
POPGRW10CY 	 2010-2018 Population: Annual Growth Rate (Esri)
HHGRW10CY 	 2010-2018 Households: Annual Growth Rate (Esri)
FAMGRW10CY 	 2010-2018 Families: Annual Growth Rate (Esri)
GENALPHACY 	 2018 Generation Alpha Population (Born 2017 or Later)
GENZ_CY 	 2018 Generation Z Population (Born 1999 to 2016)
MILLENN_CY 	 2018 Millennial Population (Born 1981 to 1998)
GENX_CY 	 2018 Generation X Population (Born 1965 to 1980)
BABYBOOMCY 	 2018 Baby Boomer Population (Born 1946 to 1964)
OLDRGENSCY 	 2018 Silent & Greatest Generations Population (Born 1945/Earlier)
GENBASE_CY 	 2018 Population by Generation Base
POP0_CY 	 2018 Total Population Age 0-4 (Esri)
POP5_CY 	 2018 Total Population Age 5-9 (Esri)
POP10_CY 	 2018 Total Population Age 10-14 (Esri)
POP15_CY 	 2018 Total Population Age 15-19 (Esri)
POP20_CY 	 2018 Total Population Age 20-24 (Esri)
POP25_CY 	 2018 Total Population Age 25-29 (Esri)
POP30_CY 	 2018 Total Population Age 30-34 (Esri)
POP35_CY 	 2018 Total Population Age 35-39 (Esri)
POP40_CY 	 2018 Total Population Age 40-44 (Esri)
POP45_CY 	 2018 Total Population Age 45-49 (Esri)
POP50_CY 	 2018 Total Population Age 50-54 (Esri)
POP55_CY 	 2018 Total Population Age 55-59 (Esri)
POP60_CY 	 2018 Total Population Age 60-64 (Esri)
POP65_CY 	 2018 Total Population Age 65-69 (Esri)
POP70_CY 	 2018 Total Population Age 70-74 (Esri)
POP75_CY 	 2018 Total Population Age 75-79 (Esri)
POP80_CY 	 2018 Total Population Age 80-84 (Esri)
POP85_CY 	 2018 Total Population Age 85+ (Esri)
POP18UP_CY 	 2018 Total Population Age 18+ (Esri)
POP21UP_CY 	 2018 Total Population Age 21+ (Esri)
MEDAGE_CY 	 2018 Median Age (Esri)
MALES_CY 	 2018 Male Population (Esri)
MALE0_CY 	 2018 Male Population Age 0-4 (Esri)
MALE5_CY 	 2018 Male Population Age 5-9 (Esri)
MALE10_CY 	 2018 Male Population Age 10-14 (Esri)
MALE15_CY 	 2018 Male Population Age 15-19 (Esri)
MALE20_CY 	 2018 Male Population Age 20-24 (Esri)
MALE25_CY 	 2018 Male Population Age 25-29 (Esri)
MALE30_CY 	 2018 Male Population Age 30-34 (Esri)
MALE35_CY 	 2018 Male Population Age 35-39 (Esri)
MALE40_CY 	 2018 Male Population Age 40-44 (Esri)
MALE45_CY 	 2018 Male Population Age 45-49 (Esri)
MALE50_CY 	 2018 Male Population Age 50-54 (Esri)
MALE55_CY 	 2018 Male Population Age 55-59 (Esri)
MALE60_CY 	 2018 Male Population Age 60-64 (Esri)
MALE65_CY 	 2018 Male Population Age 65-69 (Esri)
MALE70_CY 	 2018 Male Population Age 70-74 (Esri)
MALE75_CY 	 2018 Male Population Age 75-79 (Esri)
MALE80_CY 	 2018 Male Population Age 80-84 (Esri)
MALE85_CY 	 2018 Male Population Age 85+ (Esri)
MAL18UP_CY 	 2018 Male Population Age 18+ (Esri)
MAL21UP_CY 	 2018 Male Population Age 21+ (Esri)
MEDMAGE_CY 	 2018 Median Male Age (Esri)
FEMALES_CY 	 2018 Female Population (Esri)
FEM0_CY 	 2018 Female Population Age 0-4 (Esri)
FEM5_CY 	 2018 Female Population Age 5-9 (Esri)
FEM10_CY 	 2018 Female Population Age 10-14 (Esri)
FEM15_CY 	 2018 Female Population Age 15-19 (Esri)
FEM20_CY 	 2018 Female Population Age 20-24 (Esri)
FEM25_CY 	 2018 Female Population Age 25-29 (Esri)
FEM30_CY 	 2018 Female Population Age 30-34 (Esri)
FEM35_CY 	 2018 Female Population Age 35-39 (Esri)
FEM40_CY 	 2018 Female Population Age 40-44 (Esri)
FEM45_CY 	 2018 Female Population Age 45-49 (Esri)
FEM50_CY 	 2018 Female Population Age 50-54 (Esri)
FEM55_CY 	 2018 Female Population Age 55-59 (Esri)
FEM60_CY 	 2018 Female Population Age 60-64 (Esri)
FEM65_CY 	 2018 Female Population Age 65-69 (Esri)
FEM70_CY 	 2018 Female Population Age 70-74 (Esri)
FEM75_CY 	 2018 Female Population Age 75-79 (Esri)
FEM80_CY 	 2018 Female Population Age 80-84 (Esri)
FEM85_CY 	 2018 Female Population Age 85+ (Esri)
FEM18UP_CY 	 2018 Female Population Age 18+ (Esri)
FEM21UP_CY 	 2018 Female Population Age 21+ (Esri)
MEDFAGE_CY 	 2018 Median Female Age (Esri)
AGEBASE_CY 	 2018 Total Population by Five-Year Age Base (Esri)
AGE0_CY 	 2018 Total Population Age <1 (Esri)
AGE1_CY 	 2018 Total Population Age 1 (Esri)
AGE2_CY 	 2018 Total Population Age 2 (Esri)
AGE3_CY 	 2018 Total Population Age 3 (Esri)
AGE4_CY 	 2018 Total Population Age 4 (Esri)
AGE5_CY 	 2018 Total Population Age 5 (Esri)
AGE6_CY 	 2018 Total Population Age 6 (Esri)
AGE7_CY 	 2018 Total Population Age 7 (Esri)
AGE8_CY 	 2018 Total Population Age 8 (Esri)
AGE9_CY 	 2018 Total Population Age 9 (Esri)
AGE10_CY 	 2018 Total Population Age 10 (Esri)
AGE11_CY 	 2018 Total Population Age 11 (Esri)
AGE12_CY 	 2018 Total Population Age 12 (Esri)
AGE13_CY 	 2018 Total Population Age 13 (Esri)
AGE14_CY 	 2018 Total Population Age 14 (Esri)
AGE15_CY 	 2018 Total Population Age 15 (Esri)
AGE16_CY 	 2018 Total Population Age 16 (Esri)
AGE17_CY 	 2018 Total Population Age 17 (Esri)
AGE18_CY 	 2018 Total Population Age 18 (Esri)
AGE19_CY 	 2018 Total Population Age 19 (Esri)
AGE20_CY 	 2018 Total Population Age 20 (Esri)
AGE21_CY 	 2018 Total Population Age 21 (Esri)
AGE22_CY 	 2018 Total Population Age 22 (Esri)
AGE23_CY 	 2018 Total Population Age 23 (Esri)
AGE24_CY 	 2018 Total Population Age 24 (Esri)
AGE25_CY 	 2018 Total Population Age 25 (Esri)
AGE26_CY 	 2018 Total Population Age 26 (Esri)
AGE27_CY 	 2018 Total Population Age 27 (Esri)
AGE28_CY 	 2018 Total Population Age 28 (Esri)
AGE29_CY 	 2018 Total Population Age 29 (Esri)
AGE30_CY 	 2018 Total Population Age 30 (Esri)
AGE31_CY 	 2018 Total Population Age 31 (Esri)
AGE32_CY 	 2018 Total Population Age 32 (Esri)
AGE33_CY 	 2018 Total Population Age 33 (Esri)
AGE34_CY 	 2018 Total Population Age 34 (Esri)
AGE35_CY 	 2018 Total Population Age 35 (Esri)
AGE36_CY 	 2018 Total Population Age 36 (Esri)
AGE37_CY 	 2018 Total Population Age 37 (Esri)
AGE38_CY 	 2018 Total Population Age 38 (Esri)
AGE39_CY 	 2018 Total Population Age 39 (Esri)
AGE40_CY 	 2018 Total Population Age 40 (Esri)
AGE41_CY 	 2018 Total Population Age 41 (Esri)
AGE42_CY 	 2018 Total Population Age 42 (Esri)
AGE43_CY 	 2018 Total Population Age 43 (Esri)
AGE44_CY 	 2018 Total Population Age 44 (Esri)
AGE45_CY 	 2018 Total Population Age 45 (Esri)
AGE46_CY 	 2018 Total Population Age 46 (Esri)
AGE47_CY 	 2018 Total Population Age 47 (Esri)
AGE48_CY 	 2018 Total Population Age 48 (Esri)
AGE49_CY 	 2018 Total Population Age 49 (Esri)
AGE50_CY 	 2018 Total Population Age 50 (Esri)
AGE51_CY 	 2018 Total Population Age 51 (Esri)
AGE52_CY 	 2018 Total Population Age 52 (Esri)
AGE53_CY 	 2018 Total Population Age 53 (Esri)
AGE54_CY 	 2018 Total Population Age 54 (Esri)
AGE55_CY 	 2018 Total Population Age 55 (Esri)
AGE56_CY 	 2018 Total Population Age 56 (Esri)
AGE57_CY 	 2018 Total Population Age 57 (Esri)
AGE58_CY 	 2018 Total Population Age 58 (Esri)
AGE59_CY 	 2018 Total Population Age 59 (Esri)
AGE60_CY 	 2018 Total Population Age 60 (Esri)
AGE61_CY 	 2018 Total Population Age 61 (Esri)
AGE62_CY 	 2018 Total Population Age 62 (Esri)
AGE63_CY 	 2018 Total Population Age 63 (Esri)
AGE64_CY 	 2018 Total Population Age 64 (Esri)
AGE65_CY 	 2018 Total Population Age 65 (Esri)
AGE66_CY 	 2018 Total Population Age 66 (Esri)
AGE67_CY 	 2018 Total Population Age 67 (Esri)
AGE68_CY 	 2018 Total Population Age 68 (Esri)
AGE69_CY 	 2018 Total Population Age 69 (Esri)
AGE70_CY 	 2018 Total Population Age 70 (Esri)
AGE71_CY 	 2018 Total Population Age 71 (Esri)
AGE72_CY 	 2018 Total Population Age 72 (Esri)
AGE73_CY 	 2018 Total Population Age 73 (Esri)
AGE74_CY 	 2018 Total Population Age 74 (Esri)
AGE75_CY 	 2018 Total Population Age 75 (Esri)
AGE76_CY 	 2018 Total Population Age 76 (Esri)
AGE77_CY 	 2018 Total Population Age 77 (Esri)
AGE78_CY 	 2018 Total Population Age 78 (Esri)
AGE79_CY 	 2018 Total Population Age 79 (Esri)
AGE80_CY 	 2018 Total Population Age 80 (Esri)
AGE81_CY 	 2018 Total Population Age 81 (Esri)
AGE82_CY 	 2018 Total Population Age 82 (Esri)
AGE83_CY 	 2018 Total Population Age 83 (Esri)
AGE84_CY 	 2018 Total Population Age 84 (Esri)
MAGE0_CY 	 2018 Male Population Age <1 (Esri)
MAGE1_CY 	 2018 Male Population Age 1 (Esri)
MAGE2_CY 	 2018 Male Population Age 2 (Esri)
MAGE3_CY 	 2018 Male Population Age 3 (Esri)
MAGE4_CY 	 2018 Male Population Age 4 (Esri)
MAGE5_CY 	 2018 Male Population Age 5 (Esri)
MAGE6_CY 	 2018 Male Population Age 6 (Esri)
MAGE7_CY 	 2018 Male Population Age 7 (Esri)
MAGE8_CY 	 2018 Male Population Age 8 (Esri)
MAGE9_CY 	 2018 Male Population Age 9 (Esri)
MAGE10_CY 	 2018 Male Population Age 10 (Esri)
MAGE11_CY 	 2018 Male Population Age 11 (Esri)
MAGE12_CY 	 2018 Male Population Age 12 (Esri)
MAGE13_CY 	 2018 Male Population Age 13 (Esri)
MAGE14_CY 	 2018 Male Population Age 14 (Esri)
MAGE15_CY 	 2018 Male Population Age 15 (Esri)
MAGE16_CY 	 2018 Male Population Age 16 (Esri)
MAGE17_CY 	 2018 Male Population Age 17 (Esri)
MAGE18_CY 	 2018 Male Population Age 18 (Esri)
MAGE19_CY 	 2018 Male Population Age 19 (Esri)
MAGE20_CY 	 2018 Male Population Age 20 (Esri)
MAGE21_CY 	 2018 Male Population Age 21 (Esri)
MAGE22_CY 	 2018 Male Population Age 22 (Esri)
MAGE23_CY 	 2018 Male Population Age 23 (Esri)
MAGE24_CY 	 2018 Male Population Age 24 (Esri)
MAGE25_CY 	 2018 Male Population Age 25 (Esri)
MAGE26_CY 	 2018 Male Population Age 26 (Esri)
MAGE27_CY 	 2018 Male Population Age 27 (Esri)
MAGE28_CY 	 2018 Male Population Age 28 (Esri)
MAGE29_CY 	 2018 Male Population Age 29 (Esri)
MAGE30_CY 	 2018 Male Population Age 30 (Esri)
MAGE31_CY 	 2018 Male Population Age 31 (Esri)
MAGE32_CY 	 2018 Male Population Age 32 (Esri)
MAGE33_CY 	 2018 Male Population Age 33 (Esri)
MAGE34_CY 	 2018 Male Population Age 34 (Esri)
MAGE35_CY 	 2018 Male Population Age 35 (Esri)
MAGE36_CY 	 2018 Male Population Age 36 (Esri)
MAGE37_CY 	 2018 Male Population Age 37 (Esri)
MAGE38_CY 	 2018 Male Population Age 38 (Esri)
MAGE39_CY 	 2018 Male Population Age 39 (Esri)
MAGE40_CY 	 2018 Male Population Age 40 (Esri)
MAGE41_CY 	 2018 Male Population Age 41 (Esri)
MAGE42_CY 	 2018 Male Population Age 42 (Esri)
MAGE43_CY 	 2018 Male Population Age 43 (Esri)
MAGE44_CY 	 2018 Male Population Age 44 (Esri)
MAGE45_CY 	 2018 Male Population Age 45 (Esri)
MAGE46_CY 	 2018 Male Population Age 46 (Esri)
MAGE47_CY 	 2018 Male Population Age 47 (Esri)
MAGE48_CY 	 2018 Male Population Age 48 (Esri)
MAGE49_CY 	 2018 Male Population Age 49 (Esri)
MAGE50_CY 	 2018 Male Population Age 50 (Esri)
MAGE51_CY 	 2018 Male Population Age 51 (Esri)
MAGE52_CY 	 2018 Male Population Age 52 (Esri)
MAGE53_CY 	 2018 Male Population Age 53 (Esri)
MAGE54_CY 	 2018 Male Population Age 54 (Esri)
MAGE55_CY 	 2018 Male Population Age 55 (Esri)
MAGE56_CY 	 2018 Male Population Age 56 (Esri)
MAGE57_CY 	 2018 Male Population Age 57 (Esri)
MAGE58_CY 	 2018 Male Population Age 58 (Esri)
MAGE59_CY 	 2018 Male Population Age 59 (Esri)
MAGE60_CY 	 2018 Male Population Age 60 (Esri)
MAGE61_CY 	 2018 Male Population Age 61 (Esri)
MAGE62_CY 	 2018 Male Population Age 62 (Esri)
MAGE63_CY 	 2018 Male Population Age 63 (Esri)
MAGE64_CY 	 2018 Male Population Age 64 (Esri)
MAGE65_CY 	 2018 Male Population Age 65 (Esri)
MAGE66_CY 	 2018 Male Population Age 66 (Esri)
MAGE67_CY 	 2018 Male Population Age 67 (Esri)
MAGE68_CY 	 2018 Male Population Age 68 (Esri)
MAGE69_CY 	 2018 Male Population Age 69 (Esri)
MAGE70_CY 	 2018 Male Population Age 70 (Esri)
MAGE71_CY 	 2018 Male Population Age 71 (Esri)
MAGE72_CY 	 2018 Male Population Age 72 (Esri)
MAGE73_CY 	 2018 Male Population Age 73 (Esri)
MAGE74_CY 	 2018 Male Population Age 74 (Esri)
MAGE75_CY 	 2018 Male Population Age 75 (Esri)
MAGE76_CY 	 2018 Male Population Age 76 (Esri)
MAGE77_CY 	 2018 Male Population Age 77 (Esri)
MAGE78_CY 	 2018 Male Population Age 78 (Esri)
MAGE79_CY 	 2018 Male Population Age 79 (Esri)
MAGE80_CY 	 2018 Male Population Age 80 (Esri)
MAGE81_CY 	 2018 Male Population Age 81 (Esri)
MAGE82_CY 	 2018 Male Population Age 82 (Esri)
MAGE83_CY 	 2018 Male Population Age 83 (Esri)
MAGE84_CY 	 2018 Male Population Age 84 (Esri)
FAGE0_CY 	 2018 Female Population Age <1 (Esri)
FAGE1_CY 	 2018 Female Population Age 1 (Esri)
FAGE2_CY 	 2018 Female Population Age 2 (Esri)
FAGE3_CY 	 2018 Female Population Age 3 (Esri)
FAGE4_CY 	 2018 Female Population Age 4 (Esri)
FAGE5_CY 	 2018 Female Population Age 5 (Esri)
FAGE6_CY 	 2018 Female Population Age 6 (Esri)
FAGE7_CY 	 2018 Female Population Age 7 (Esri)
FAGE8_CY 	 2018 Female Population Age 8 (Esri)
FAGE9_CY 	 2018 Female Population Age 9 (Esri)
FAGE10_CY 	 2018 Female Population Age 10 (Esri)
FAGE11_CY 	 2018 Female Population Age 11 (Esri)
FAGE12_CY 	 2018 Female Population Age 12 (Esri)
FAGE13_CY 	 2018 Female Population Age 13 (Esri)
FAGE14_CY 	 2018 Female Population Age 14 (Esri)
FAGE15_CY 	 2018 Female Population Age 15 (Esri)
FAGE16_CY 	 2018 Female Population Age 16 (Esri)
FAGE17_CY 	 2018 Female Population Age 17 (Esri)
FAGE18_CY 	 2018 Female Population Age 18 (Esri)
FAGE19_CY 	 2018 Female Population Age 19 (Esri)
FAGE20_CY 	 2018 Female Population Age 20 (Esri)
FAGE21_CY 	 2018 Female Population Age 21 (Esri)
FAGE22_CY 	 2018 Female Population Age 22 (Esri)
FAGE23_CY 	 2018 Female Population Age 23 (Esri)
FAGE24_CY 	 2018 Female Population Age 24 (Esri)
FAGE25_CY 	 2018 Female Population Age 25 (Esri)
FAGE26_CY 	 2018 Female Population Age 26 (Esri)
FAGE27_CY 	 2018 Female Population Age 27 (Esri)
FAGE28_CY 	 2018 Female Population Age 28 (Esri)
FAGE29_CY 	 2018 Female Population Age 29 (Esri)
FAGE30_CY 	 2018 Female Population Age 30 (Esri)
FAGE31_CY 	 2018 Female Population Age 31 (Esri)
FAGE32_CY 	 2018 Female Population Age 32 (Esri)
FAGE33_CY 	 2018 Female Population Age 33 (Esri)
FAGE34_CY 	 2018 Female Population Age 34 (Esri)
FAGE35_CY 	 2018 Female Population Age 35 (Esri)
FAGE36_CY 	 2018 Female Population Age 36 (Esri)
FAGE37_CY 	 2018 Female Population Age 37 (Esri)
FAGE38_CY 	 2018 Female Population Age 38 (Esri)
FAGE39_CY 	 2018 Female Population Age 39 (Esri)
FAGE40_CY 	 2018 Female Population Age 40 (Esri)
FAGE41_CY 	 2018 Female Population Age 41 (Esri)
FAGE42_CY 	 2018 Female Population Age 42 (Esri)
FAGE43_CY 	 2018 Female Population Age 43 (Esri)
FAGE44_CY 	 2018 Female Population Age 44 (Esri)
FAGE45_CY 	 2018 Female Population Age 45 (Esri)
FAGE46_CY 	 2018 Female Population Age 46 (Esri)
FAGE47_CY 	 2018 Female Population Age 47 (Esri)
FAGE48_CY 	 2018 Female Population Age 48 (Esri)
FAGE49_CY 	 2018 Female Population Age 49 (Esri)
FAGE50_CY 	 2018 Female Population Age 50 (Esri)
FAGE51_CY 	 2018 Female Population Age 51 (Esri)
FAGE52_CY 	 2018 Female Population Age 52 (Esri)
FAGE53_CY 	 2018 Female Population Age 53 (Esri)
FAGE54_CY 	 2018 Female Population Age 54 (Esri)
FAGE55_CY 	 2018 Female Population Age 55 (Esri)
FAGE56_CY 	 2018 Female Population Age 56 (Esri)
FAGE57_CY 	 2018 Female Population Age 57 (Esri)
FAGE58_CY 	 2018 Female Population Age 58 (Esri)
FAGE59_CY 	 2018 Female Population Age 59 (Esri)
FAGE60_CY 	 2018 Female Population Age 60 (Esri)
FAGE61_CY 	 2018 Female Population Age 61 (Esri)
FAGE62_CY 	 2018 Female Population Age 62 (Esri)
FAGE63_CY 	 2018 Female Population Age 63 (Esri)
FAGE64_CY 	 2018 Female Population Age 64 (Esri)
FAGE65_CY 	 2018 Female Population Age 65 (Esri)
FAGE66_CY 	 2018 Female Population Age 66 (Esri)
FAGE67_CY 	 2018 Female Population Age 67 (Esri)
FAGE68_CY 	 2018 Female Population Age 68 (Esri)
FAGE69_CY 	 2018 Female Population Age 69 (Esri)
FAGE70_CY 	 2018 Female Population Age 70 (Esri)
FAGE71_CY 	 2018 Female Population Age 71 (Esri)
FAGE72_CY 	 2018 Female Population Age 72 (Esri)
FAGE73_CY 	 2018 Female Population Age 73 (Esri)
FAGE74_CY 	 2018 Female Population Age 74 (Esri)
FAGE75_CY 	 2018 Female Population Age 75 (Esri)
FAGE76_CY 	 2018 Female Population Age 76 (Esri)
FAGE77_CY 	 2018 Female Population Age 77 (Esri)
FAGE78_CY 	 2018 Female Population Age 78 (Esri)
FAGE79_CY 	 2018 Female Population Age 79 (Esri)
FAGE80_CY 	 2018 Female Population Age 80 (Esri)
FAGE81_CY 	 2018 Female Population Age 81 (Esri)
FAGE82_CY 	 2018 Female Population Age 82 (Esri)
FAGE83_CY 	 2018 Female Population Age 83 (Esri)
FAGE84_CY 	 2018 Female Population Age 84 (Esri)
WAGEBASECY 	 2018 White Population by Age Base (Esri)
WHTMBASECY 	 2018 White Male Population by Age Base (Esri)
WHTFBASECY 	 2018 White Female Population by Age Base (Esri)
BAGEBASECY 	 2018 Black Population by Age Base (Esri)
BLKMBASECY 	 2018 Black Male Population by Age Base (Esri)
BLKFBASECY 	 2018 Black Female Population by Age Base (Esri)
IAGEBASECY 	 2018 American Indian/Alaska Native Population by Age Base (Esri)
AIMBASE_CY 	 2018 American Indian/Alaska Native Male Population by Age Base (Esri)
AIFBASE_CY 	 2018 American Indian/Alaska Native Female Population by Age Base (Esri)
AAGEBASECY 	 2018 Asian Population by Age Base (Esri)
ASNMBASECY 	 2018 Asian Male Population by Age Base (Esri)
ASNFBASECY 	 2018 Asian Female Population by Age Base (Esri)
PAGEBASECY 	 2018 Pacific Islander Population by Age Base (Esri)
PIMBASE_CY 	 2018 Pacific Islander Male Population by Age Base (Esri)
PIFBASE_CY 	 2018 Pacific Islander Female Population by Age Base (Esri)
OAGEBASECY 	 2018 Other Race Population by Age Base (Esri)
OTHMBASECY 	 2018 Other Race Male Population by Age Base (Esri)
OTHFBASECY 	 2018 Other Race Female Population by Age Base (Esri)
MAGEBASECY 	 2018 Multiple Races Population by Age Base (Esri)
MLTMBASECY 	 2018 Multiple Races Male Population by Age Base (Esri)
MLTFBASECY 	 2018 Multiple Races Female Population by Age Base (Esri)
HAGEBASECY 	 2018 Hispanic Population by Age Base (Esri)
HSPMBASECY 	 2018 Hispanic Male Population by Age Base (Esri)
HSPFBASECY 	 2018 Hispanic Female Population by Age Base (Esri)
CIVLBFR_CY 	 2018 Civilian Population Age 16+ in Labor Force (Esri)
EMP_CY 	 2018 Employed Civilian Population Age 16+ (Esri)
INDAGRI_CY 	 2018 Industry: Agriculture/Forestry/Fishing/Hunting (Esri)
INDMIN_CY 	 2018 Industry: Mining/Quarrying/Oil & Gas Extraction (Esri)
INDCONS_CY 	 2018 Industry: Construction (Esri)
INDMANU_CY 	 2018 Industry: Manufacturing (Esri)
INDWHTR_CY 	 2018 Industry: Wholesale Trade (Esri)
INDRTTR_CY 	 2018 Industry: Retail Trade (Esri)
INDTRAN_CY 	 2018 Industry: Transportation/Warehousing (Esri)
INDUTIL_CY 	 2018 Industry: Utilities (Esri)
INDINFO_CY 	 2018 Industry: Information (Esri)
INDFIN_CY 	 2018 Industry: Finance/Insurance (Esri)
INDRE_CY 	 2018 Industry: Real Estate/Rental/Leasing (Esri)
INDTECH_CY 	 2018 Industry: Professional/Scientific/Tech Services (Esri)
INDMGMT_CY 	 2018 Industry: Management of Companies/Enterprises (Esri)
INDADMN_CY 	 2018 Industry: Admin/Support/Waste Management Services (Esri)
INDEDUC_CY 	 2018 Industry: Educational Services (Esri)
INDHLTH_CY 	 2018 Industry: Health Care/Social Assistance (Esri)
INDARTS_CY 	 2018 Industry: Arts/Entertainment/Recreation (Esri)
INDFOOD_CY 	 2018 Industry: Accommodation/Food Services (Esri)
INDOTSV_CY 	 2018 Industry: Other Services (excl Public Administration) (Esri)
INDPUBL_CY 	 2018 Industry: Public Administration (Esri)
INDBASE_CY 	 2018 Employed Civilian Population Age 16+ by Industry Base (Esri)
UNEMP_CY 	 2018 Unemployed Population Age 16+ (Esri)
UNEMPRT_CY 	 2018 Unemployment Rate (Esri)
OCCMGMT_CY 	 2018 Occupation: Management (Esri)
OCCBUS_CY 	 2018 Occupation: Business/Financial (Esri)
OCCCOMP_CY 	 2018 Occupation: Computer/Mathematical (Esri)
OCCARCH_CY 	 2018 Occupation: Architecture/Engineering (Esri)
OCCSSCI_CY 	 2018 Occupation: Life/Physical/Social Science (Esri)
OCCSSRV_CY 	 2018 Occupation: Community/Social Service (Esri)
OCCLEGL_CY 	 2018 Occupation: Legal (Esri)
OCCEDUC_CY 	 2018 Occupation: Education/Training/Library (Esri)
OCCENT_CY 	 2018 Occupation: Arts/Design/Entertainment/Sports/Media (Esri)
OCCHTCH_CY 	 2018 Occupation: Healthcare Practitioner/Technician (Esri)
OCCHLTH_CY 	 2018 Occupation: Healthcare Support (Esri)
OCCPROT_CY 	 2018 Occupation: Protective Service (Esri)
OCCFOOD_CY 	 2018 Occupation: Food Preparation/Serving Related (Esri)
OCCBLDG_CY 	 2018 Occupation: Building/Grounds Cleaning/Maintenance (Esri)
OCCPERS_CY 	 2018 Occupation: Personal Care/Service (Esri)
OCCSALE_CY 	 2018 Occupation: Sales and Sales Related (Esri)
OCCADMN_CY 	 2018 Occupation: Office/Administrative Support (Esri)
OCCFARM_CY 	 2018 Occupation: Farming/Fishing/Forestry (Esri)
OCCCONS_CY 	 2018 Occupation: Construction/Extraction (Esri)
OCCMAIN_CY 	 2018 Occupation: Installation/Maintenance/Repair (Esri)
OCCPROD_CY 	 2018 Occupation: Production (Esri)
OCCTRAN_CY 	 2018 Occupation: Transportation/Material Moving (Esri)
OCCBASE_CY 	 2018 Employed Civilian Population Age 16+ by Occupation Base (Esri)
WHITE_CY 	 2018 White Population (Esri)
BLACK_CY 	 2018 Black/African American Population (Esri)
AMERIND_CY 	 2018 American Indian/Alaska Native Population (Esri)
ASIAN_CY 	 2018 Asian Population (Esri)
PACIFIC_CY 	 2018 Pacific Islander Population (Esri)
OTHRACE_CY 	 2018 Other Race Population (Esri)
RACE2UP_CY 	 2018 Population of Two or More Races (Esri)
HISPPOP_CY 	 2018 Hispanic Population (Esri)
HISPWHT_CY 	 2018 Hispanic White Population (Esri)
HISPBLK_CY 	 2018 Hispanic Black/African American Population (Esri)
HISPAI_CY 	 2018 Hispanic American Indian/Alaska Native Population (Esri)
HISPASN_CY 	 2018 Hispanic Asian Population (Esri)
HISPPI_CY 	 2018 Hispanic Pacific Islander Population (Esri)
HISPOTH_CY 	 2018 Hispanic Other Race Population (Esri)
HISPMLT_CY 	 2018 Hispanic Population of Two or More Races (Esri)
NONHISP_CY 	 2018 Non-Hispanic Population (Esri)
NHSPWHT_CY 	 2018 White Non-Hispanic Population (Esri)
NHSPBLK_CY 	 2018 Black/African American Non-Hispanic Population (Esri)
NHSPAI_CY 	 2018 American Indian/Alaska Native Non-Hispanic Population (Esri)
NHSPASN_CY 	 2018 Asian Non-Hispanic Population (Esri)
NHSPPI_CY 	 2018 Pacific Islander Non-Hispanic Population (Esri)
NHSPOTH_CY 	 2018 Other Race Non-Hispanic Population (Esri)
NHSPMLT_CY 	 2018 Multiple Races Non-Hispanic Population (Esri)
MINORITYCY 	 2018 Minority Population (Esri)
DIVINDX_CY 	 2018 Diversity Index (Esri)
RACEBASECY 	 2018 Population by Race Base (Esri)
NOHS_CY 	 2018 Education: Less than 9th Grade (Esri)
SOMEHS_CY 	 2018 Education: 9-12th Grade/No Diploma (Esri)
HSGRAD_CY 	 2018 Education: High School Diploma (Esri)
GED_CY 	 2018 Education: GED/Alternative Credential (Esri)
SMCOLL_CY 	 2018 Education: Some College/No Degree (Esri)
ASSCDEG_CY 	 2018 Education: Associate's Degree (Esri)
BACHDEG_CY 	 2018 Education: Bachelor's Degree (Esri)
GRADDEG_CY 	 2018 Education: Graduate/Professional Degree (Esri)
EDUCBASECY 	 2018 Educational Attainment Base (Esri)
NEVMARR_CY 	 2018 Population Age 15+: Never Married (Esri)
MARRIED_CY 	 2018 Population Age 15+: Married (Esri)
WIDOWED_CY 	 2018 Population Age 15+: Widowed (Esri)
DIVORCD_CY 	 2018 Population Age 15+: Divorced (Esri)
MARBASE_CY 	 2018 Marital Status Base (Esri)
HINC0_CY 	 2018 Household Income less than $15,000 (Esri)
HINC15_CY 	 2018 Household Income $15,000-$24,999 (Esri)
HINC25_CY 	 2018 Household Income $25,000-$34,999 (Esri)
HINC35_CY 	 2018 Household Income $35,000-$49,999 (Esri)
HINC50_CY 	 2018 Household Income $50,000-$74,999 (Esri)
HINC75_CY 	 2018 Household Income $75,000-$99,999 (Esri)
HINC100_CY 	 2018 Household Income $100,000-$149,999 (Esri)
HINC150_CY 	 2018 Household Income $150,000-$199,999 (Esri)
HINC200_CY 	 2018 Household Income $200,000 or greater (Esri)
MEDHINC_CY 	 2018 Median Household Income (Esri)
AVGHINC_CY 	 2018 Average Household Income (Esri)
PCI_CY 	 2018 Per Capita Income (Esri)
AGGINC_CY 	 2018 Aggregate Income (Esri)
AGGHINC_CY 	 2018 Aggregate Household Income (Esri)
HINCBASECY 	 2018 Households by Income Base (Esri)
A15I0_CY 	 2018 Household Income less than $15,000 and Householder Age 15-24 (Esri)
A15I15_CY 	 2018 Household Income $15,000-$24,999 and Householder Age 15-24 (Esri)
A15I25_CY 	 2018 Household Income $25,000-$34,999 and Householder Age 15-24 (Esri)
A15I35_CY 	 2018 Household Income $35,000-$49,999 and Householder Age 15-24 (Esri)
A15I50_CY 	 2018 Household Income $50,000-$74,999 and Householder Age 15-24 (Esri)
A15I75_CY 	 2018 Household Income $75,000-$99,999 and Householder Age 15-24 (Esri)
A15I100_CY 	 2018 Household Income $100,000-$149,999 and Householder Age 15-24 (Esri)
A15I150_CY 	 2018 Household Income $150,000-$199,999 and Householder Age 15-24 (Esri)
A15I200_CY 	 2018 Household Income $200,000+ and Householder Age 15-24 (Esri)
MEDIA15_CY 	 2018 Median Household Income and Householder Age 15-24 (Esri)
AVGIA15_CY 	 2018 Average Household Income and Householder Age 15-24 (Esri)
IA15BASECY 	 2018 Households by Income Base and Householder Age 15-24 (Esri)
AGGIA15_CY 	 2018 Aggregate Household Income and Householder Age 15-24 (Esri)
A25I0_CY 	 2018 Household Income less than $15,000 and Householder Age 25-34 (Esri)
A25I15_CY 	 2018 Household Income $15,000-$24,999 and Householder Age 25-34 (Esri)
A25I25_CY 	 2018 Household Income $25,000-$34,999 and Householder Age 25-34 (Esri)
A25I35_CY 	 2018 Household Income $35,000-$49,999 and Householder Age 25-34 (Esri)
A25I50_CY 	 2018 Household Income $50,000-$74,999 and Householder Age 25-34 (Esri)
A25I75_CY 	 2018 Household Income $75,000-$99,999 and Householder Age 25-34 (Esri)
A25I100_CY 	 2018 Household Income $100,000-$149,999 and Householder Age 25-34 (Esri)
A25I150_CY 	 2018 Household Income $150,000-$199,999 and Householder Age 25-34 (Esri)
A25I200_CY 	 2018 Household Income $200,000+ and Householder Age 25-34 (Esri)
MEDIA25_CY 	 2018 Median Household Income and Householder Age 25-34 (Esri)
AVGIA25_CY 	 2018 Average Household Income and Householder Age 25-34 (Esri)
IA25BASECY 	 2018 Households by Income Base and Householder Age 25-34 (Esri)
AGGIA25_CY 	 2018 Aggregate Household Income and Householder Age 25-34 (Esri)
A35I0_CY 	 2018 Household Income less than $15,000 and Householder Age 35-44 (Esri)
A35I15_CY 	 2018 Household Income $15,000-$24,999 and Householder Age 35-44 (Esri)
A35I25_CY 	 2018 Household Income $25,000-$34,999 and Householder Age 35-44 (Esri)
A35I35_CY 	 2018 Household Income $35,000-$49,999 and Householder Age 35-44 (Esri)
A35I50_CY 	 2018 Household Income $50,000-$74,999 and Householder Age 35-44 (Esri)
A35I75_CY 	 2018 Household Income $75,000-$99,999 and Householder Age 35-44 (Esri)
A35I100_CY 	 2018 Household Income $100,000-$149,999 and Householder Age 35-44 (Esri)
A35I150_CY 	 2018 Household Income $150,000-$199,999 and Householder Age 35-44 (Esri)
A35I200_CY 	 2018 Household Income $200,000+ and Householder Age 35-44 (Esri)
MEDIA35_CY 	 2018 Median Household Income and Householder Age 35-44 (Esri)
AVGIA35_CY 	 2018 Average Household Income and Householder Age 35-44 (Esri)
IA35BASECY 	 2018 Households by Income Base and Householder Age 35-44 (Esri)
AGGIA35_CY 	 2018 Aggregate Household Income and Householder Age 35-44 (Esri)
A45I0_CY 	 2018 Household Income less than $15,000 and Householder Age 45-54 (Esri)
A45I15_CY 	 2018 Household Income $15,000-$24,999 and Householder Age 45-54 (Esri)
A45I25_CY 	 2018 Household Income $25,000-$34,999 and Householder Age 45-54 (Esri)
A45I35_CY 	 2018 Household Income $35,000-$49,999 and Householder Age 45-54 (Esri)
A45I50_CY 	 2018 Household Income $50,000-$74,999 and Householder Age 45-54 (Esri)
A45I75_CY 	 2018 Household Income $75,000-$99,999 and Householder Age 45-54 (Esri)
A45I100_CY 	 2018 Household Income $100,000-$149,999 and Householder Age 45-54 (Esri)
A45I150_CY 	 2018 Household Income $150,000-$199,999 and Householder Age 45-54 (Esri)
A45I200_CY 	 2018 Household Income $200,000+ and Householder Age 45-54 (Esri)
MEDIA45_CY 	 2018 Median Household Income and Householder Age 45-54 (Esri)
AVGIA45_CY 	 2018 Average Household Income and Householder Age 45-54 (Esri)
IA45BASECY 	 2018 Households by Income Base and Householder Age 45-54 (Esri)
AGGIA45_CY 	 2018 Aggregate Household Income and Householder Age 45-54 (Esri)
A55I0_CY 	 2018 Household Income less than $15,000 and Householder Age 55-64 (Esri)
A55I15_CY 	 2018 Household Income $15,000-$24,999 and Householder Age 55-64 (Esri)
A55I25_CY 	 2018 Household Income $25,000-$34,999 and Householder Age 55-64 (Esri)
A55I35_CY 	 2018 Household Income $35,000-$49,999 and Householder Age 55-64 (Esri)
A55I50_CY 	 2018 Household Income $50,000-$74,999 and Householder Age 55-64 (Esri)
A55I75_CY 	 2018 Household Income $75,000-$99,999 and Householder Age 55-64 (Esri)
A55I100_CY 	 2018 Household Income $100,000-$149,999 and Householder Age 55-64 (Esri)
A55I150_CY 	 2018 Household Income $150,000-$199,999 and Householder Age 55-64 (Esri)
A55I200_CY 	 2018 Household Income $200,000+ and Householder Age 55-64 (Esri)
MEDIA55_CY 	 2018 Median Household Income and Householder Age 55-64 (Esri)
AVGIA55_CY 	 2018 Average Household Income and Householder Age 55-64 (Esri)
IA55BASECY 	 2018 Households by Income Base and Householder Age 55-64 (Esri)
AGGIA55_CY 	 2018 Aggregate Household Income and Householder Age 55-64 (Esri)
A65I0_CY 	 2018 Household Income less than $15,000 and Householder Age 65-74 (Esri)
A65I15_CY 	 2018 Household Income $15,000-$24,999 and Householder Age 65-74 (Esri)
A65I25_CY 	 2018 Household Income $25,000-$34,999 and Householder Age 65-74 (Esri)
A65I35_CY 	 2018 Household Income $35,000-$49,999 and Householder Age 65-74 (Esri)
A65I50_CY 	 2018 Household Income $50,000-$74,999 and Householder Age 65-74 (Esri)
A65I75_CY 	 2018 Household Income $75,000-$99,999 and Householder Age 65-74 (Esri)
A65I100_CY 	 2018 Household Income $100,000-$149,999 and Householder Age 65-74 (Esri)
A65I150_CY 	 2018 Household Income $150,000-$199,999 and Householder Age 65-74 (Esri)
A65I200_CY 	 2018 Household Income $200,000+ and Householder Age 65-74 (Esri)
MEDIA65_CY 	 2018 Median Household Income and Householder Age 65-74 (Esri)
AVGIA65_CY 	 2018 Average Household Income and Householder Age 65-74 (Esri)
IA65BASECY 	 2018 Households by Income Base and Householder Age 65-74 (Esri)
AGGIA65_CY 	 2018 Aggregate Household Income and Householder Age 65-74 (Esri)
A75I0_CY 	 2018 Household Income less than $15,000 and Householder Age 75+ (Esri)
A75I15_CY 	 2018 Household Income $15,000-$24,999 and Householder Age 75+ (Esri)
A75I25_CY 	 2018 Household Income $25,000-$34,999 and Householder Age 75+ (Esri)
A75I35_CY 	 2018 Household Income $35,000-$49,999 and Householder Age 75+ (Esri)
A75I50_CY 	 2018 Household Income $50,000-$74,999 and Householder Age 75+ (Esri)
A75I75_CY 	 2018 Household Income $75,000-$99,999 and Householder Age 75+ (Esri)
A75I100_CY 	 2018 Household Income $100,000-$149,999 and Householder Age 75+ (Esri)
A75I150_CY 	 2018 Household Income $150,000-$199,999 and Householder Age 75+ (Esri)
A75I200_CY 	 2018 Household Income $200,000+ and Householder Age 75+ (Esri)
MEDIA75_CY 	 2018 Median Household Income and Householder Age 75+ (Esri)
AVGIA75_CY 	 2018 Average Household Income and Householder Age 75+ (Esri)
IA75BASECY 	 2018 Households by Income Base and Householder Age 75+ (Esri)
AGGIA75_CY 	 2018 Aggregate Household Income and Householder Age 75+ (Esri)
MEDHHR_CY 	 2018 Median Age of Householder (Esri)
MEDIA55UCY 	 2018 Median Household Income and Householder Age 55+ (Esri)
AVGIA55UCY 	 2018 Average Household Income and Householder Age 55+ (Esri)
IA55UBASCY 	 2018 Households by Income Base and Householder Age 55+ (Esri)
MEDIA65UCY 	 2018 Median Household Income and Householder Age 65+ (Esri)
AVGIA65UCY 	 2018 Average Household Income and Householder Age 65+ (Esri)
IA65UBASCY 	 2018 Households by Income Base and Householder Age 65+ (Esri)
DI0_CY 	 2018 Disposable Income less than $15,000 (Esri)
DI15_CY 	 2018 Disposable Income $15,000-$24,999 (Esri)
DI25_CY 	 2018 Disposable Income $25,000-$34,999 (Esri)
DI35_CY 	 2018 Disposable Income $35,000-$49,999 (Esri)
DI50_CY 	 2018 Disposable Income $50,000-$74,999 (Esri)
DI75_CY 	 2018 Disposable Income $75,000-$99,999 (Esri)
DI100_CY 	 2018 Disposable Income $100,000-$149,999 (Esri)
DI150_CY 	 2018 Disposable Income $150,000-$199,999 (Esri)
DI200_CY 	 2018 Disposable Income $200,000 or greater (Esri)
AGGDI_CY 	 2018 Aggregate Disposable Income
MEDDI_CY 	 2018 Median Disposable Income (Esri)
AVGDI_CY 	 2018 Average Disposable Income (Esri)
A15DI0_CY 	 2018 Disposable Income less than $15,000 and Householder Age 15-24 (Esri)
A15DI15_CY 	 2018 Disposable Income $15,000-$24,999 and Householder Age 15-24 (Esri)
A15DI25_CY 	 2018 Disposable Income $25,000-$34,999 and Householder Age 15-24 (Esri)
A15DI35_CY 	 2018 Disposable Income $35,000-$49,999 and Householder Age 15-24 (Esri)
A15DI50_CY 	 2018 Disposable Income $50,000-$74,999 and Householder Age 15-24 (Esri)
A15DI75_CY 	 2018 Disposable Income $75,000-$99,999 and Householder Age 15-24 (Esri)
A15DI100CY 	 2018 Disposable Income $100,000-$149,999 and Householder Age 15-24 (Esri)
A15DI150CY 	 2018 Disposable Income $150,000-$199,999 and Householder Age 15-24 (Esri)
A15DI200CY 	 2018 Disposable Income $200,000+ and Householder Age 15-24 (Esri)
AGGDIA15CY 	 2018 Aggregate Disposable Income: Householder 15-24
MEDDIA15CY 	 2018 Median Disposable Income and Householder Age 15-24 (Esri)
AVGDIA15CY 	 2018 Average Disposable Income and Householder Age 15-24 (Esri)
A25DI0_CY 	 2018 Disposable Income less than $15,000 and Householder Age 25-34 (Esri)
A25DI15_CY 	 2018 Disposable Income $15,000-$24,999 and Householder Age 25-34 (Esri)
A25DI25_CY 	 2018 Disposable Income $25,000-$34,999 and Householder Age 25-34 (Esri)
A25DI35_CY 	 2018 Disposable Income $35,000-$49,999 and Householder Age 25-34 (Esri)
A25DI50_CY 	 2018 Disposable Income $50,000-$74,999 and Householder Age 25-34 (Esri)
A25DI75_CY 	 2018 Disposable Income $75,000-$99,999 and Householder Age 25-34 (Esri)
A25DI100CY 	 2018 Disposable Income $100,000-$149,999 and Householder Age 25-34 (Esri)
A25DI150CY 	 2018 Disposable Income $150,000-$199,999 and Householder Age 25-34 (Esri)
A25DI200CY 	 2018 Disposable Income $200,000+ and Householder Age 25-34 (Esri)
AGGDIA25CY 	 2018 Aggregate Disposable Income: Householder 25-34
MEDDIA25CY 	 2018 Median Disposable Income and Householder Age 25-34 (Esri)
AVGDIA25CY 	 2018 Average Disposable Income and Householder Age 25-34 (Esri)
A35DI0_CY 	 2018 Disposable Income less than $15,000 and Householder Age 35-44 (Esri)
A35DI15_CY 	 2018 Disposable Income $15,000-$24,999 and Householder Age 35-44 (Esri)
A35DI25_CY 	 2018 Disposable Income $25,000-$34,999 and Householder Age 35-44 (Esri)
A35DI35_CY 	 2018 Disposable Income $35,000-$49,999 and Householder Age 35-44 (Esri)
A35DI50_CY 	 2018 Disposable Income $50,000-$74,999 and Householder Age 35-44 (Esri)
A35DI75_CY 	 2018 Disposable Income $75,000-$99,999 and Householder Age 35-44 (Esri)
A35DI100CY 	 2018 Disposable Income $100,000-$149,999 and Householder Age 35-44 (Esri)
A35DI150CY 	 2018 Disposable Income $150,000-$199,999 and Householder Age 35-44 (Esri)
A35DI200CY 	 2018 Disposable Income $200,000+ and Householder Age 35-44 (Esri)
AGGDIA35CY 	 2018 Aggregate Disposable Income: Householder 35-44
MEDDIA35CY 	 2018 Median Disposable Income and Householder Age 35-44 (Esri)
AVGDIA35CY 	 2018 Average Disposable Income and Householder Age 35-44 (Esri)
A45DI0_CY 	 2018 Disposable Income less than $15,000 and Householder Age 45-54 (Esri)
A45DI15_CY 	 2018 Disposable Income $15,000-$24,999 and Householder Age 45-54 (Esri)
A45DI25_CY 	 2018 Disposable Income $25,000-$34,999 and Householder Age 45-54 (Esri)
A45DI35_CY 	 2018 Disposable Income $35,000-$49,999 and Householder Age 45-54 (Esri)
A45DI50_CY 	 2018 Disposable Income $50,000-$74,999 and Householder Age 45-54 (Esri)
A45DI75_CY 	 2018 Disposable Income $75,000-$99,999 and Householder Age 45-54 (Esri)
A45DI100CY 	 2018 Disposable Income $100,000-$149,999 and Householder Age 45-54 (Esri)
A45DI150CY 	 2018 Disposable Income $150,000-$199,999 and Householder Age 45-54 (Esri)
A45DI200CY 	 2018 Disposable Income $200,000+ and Householder Age 45-54 (Esri)
AGGDIA45CY 	 2018 Aggregate Disposable Income: Householder 45-54
MEDDIA45CY 	 2018 Median Disposable Income and Householder Age 45-54 (Esri)
AVGDIA45CY 	 2018 Average Disposable Income and Householder Age 45-54 (Esri)
A55DI0_CY 	 2018 Disposable Income less than $15,000 and Householder Age 55-64 (Esri)
A55DI15_CY 	 2018 Disposable Income $15,000-$24,999 and Householder Age 55-64 (Esri)
A55DI25_CY 	 2018 Disposable Income $25,000-$34,999 and Householder Age 55-64 (Esri)
A55DI35_CY 	 2018 Disposable Income $35,000-$49,999 and Householder Age 55-64 (Esri)
A55DI50_CY 	 2018 Disposable Income $50,000-$74,999 and Householder Age 55-64 (Esri)
A55DI75_CY 	 2018 Disposable Income $75,000-$99,999 and Householder Age 55-64 (Esri)
A55DI100CY 	 2018 Disposable Income $100,000-$149,999 and Householder Age 55-64 (Esri)
A55DI150CY 	 2018 Disposable Income $150,000-$199,999 and Householder Age 55-64 (Esri)
A55DI200CY 	 2018 Disposable Income $200,000+ and Householder Age 55-64 (Esri)
AGGDIA55CY 	 2018 Aggregate Disposable Income: Householder 55-64
MEDDIA55CY 	 2018 Median Disposable Income and Householder Age 55-64 (Esri)
AVGDIA55CY 	 2018 Average Disposable Income and Householder Age 55-64 (Esri)
A65DI0_CY 	 2018 Disposable Income less than $15,000 and Householder Age 65-74 (Esri)
A65DI15_CY 	 2018 Disposable Income $15,000-$24,999 and Householder Age 65-74 (Esri)
A65DI25_CY 	 2018 Disposable Income $25,000-$34,999 and Householder Age 65-74 (Esri)
A65DI35_CY 	 2018 Disposable Income $35,000-$49,999 and Householder Age 65-74 (Esri)
A65DI50_CY 	 2018 Disposable Income $50,000-$74,999 and Householder Age 65-74 (Esri)
A65DI75_CY 	 2018 Disposable Income $75,000-$99,999 and Householder Age 65-74 (Esri)
A65DI100CY 	 2018 Disposable Income $100,000-$149,999 and Householder Age 65-74 (Esri)
A65DI150CY 	 2018 Disposable Income $150,000-$199,999 and Householder Age 65-74 (Esri)
A65DI200CY 	 2018 Disposable Income $200,000+ and Householder Age 65-74 (Esri)
AGGDIA65CY 	 2018 Aggregate Disposable Income: Householder 65-74
MEDDIA65CY 	 2018 Median Disposable Income and Householder Age 65-74 (Esri)
AVGDIA65CY 	 2018 Average Disposable Income and Householder Age 65-74 (Esri)
A75DI0_CY 	 2018 Disposable Income less than $15,000 and Householder Age 75+ (Esri)
A75DI15_CY 	 2018 Disposable Income $15,000-$24,999 and Householder Age 75+ (Esri)
A75DI25_CY 	 2018 Disposable Income $25,000-$34,999 and Householder Age 75+ (Esri)
A75DI35_CY 	 2018 Disposable Income $35,000-$49,999 and Householder Age 75+ (Esri)
A75DI50_CY 	 2018 Disposable Income $50,000-$74,999 and Householder Age 75+ (Esri)
A75DI75_CY 	 2018 Disposable Income $75,000-$99,999 and Householder Age 75+ (Esri)
A75DI100CY 	 2018 Disposable Income $100,000-$149,999 and Householder Age 75+ (Esri)
A75DI150CY 	 2018 Disposable Income $150,000-$199,999 and Householder Age 75+ (Esri)
A75DI200CY 	 2018 Disposable Income $200,000 or greater and Householder Age 75+ (Esri)
AGGDIA75CY 	 2018 Aggregate Disposable Income: Householder 75+
MEDDIA75CY 	 2018 Median Disposable Income and Householder Age 75+ (Esri)
AVGDIA75CY 	 2018 Average Disposable Income and Householder Age 75+ (Esri)
DIA15BASCY 	 2018 HHs by Disposable Income Base and Householder Age 15-24 (Esri)
DIA25BASCY 	 2018 HHs by Disposable Income Base and Householder Age 25-34 (Esri)
DIA35BASCY 	 2018 HHs by Disposable Income Base and Householder Age 35-44 (Esri)
DIA45BASCY 	 2018 HHs by Disposable Income Base and Householder Age 45-54 (Esri)
DIA55BASCY 	 2018 HHs by Disposable Income Base and Householder Age 55-64 (Esri)
DIA65BASCY 	 2018 HHs by Disposable Income Base and Householder Age 65-74 (Esri)
DIA75BASCY 	 2018 Households by Disposable Income Base and Householder Age 75+ (Esri)
DIBASE_CY 	 2018 Households by Disposable Income Base (Esri)
NW0_CY 	 2018 Net Worth less than $15,000 (Esri)
NW15_CY 	 2018 Net Worth $15,000-$34,999 (Esri)
NW35_CY 	 2018 Net Worth $35,000-$49,999 (Esri)
NW50_CY 	 2018 Net Worth $50,000-$74,999 (Esri)
NW75_CY 	 2018 Net Worth $75,000-$99,999 (Esri)
NW100_CY 	 2018 Net Worth $100,000-$149,999 (Esri)
NW150_CY 	 2018 Net Worth $150,000-$249,999 (Esri)
NW250_CY 	 2018 Net Worth $250,000-$499,999 (Esri)
NW500_CY 	 2018 Net Worth $500,000 or greater (Esri)
AGGNW_CY 	 2018 Aggregate Net Worth
MEDNW_CY 	 2018 Median Net Worth (Esri)
AVGNW_CY 	 2018 Average Net Worth (Esri)
NWBASE_CY 	 2018 Households by Net Worth Base (Esri)
A15NW0_CY 	 2018 Net Worth less than $15,000 and Householder Age 15-24 (Esri)
A15NW15_CY 	 2018 Net Worth $15,000-$34,999 and Householder Age 15-24 (Esri)
A15NW35_CY 	 2018 Net Worth $35,000-$49,999 and Householder Age 15-24 (Esri)
A15NW50_CY 	 2018 Net Worth $50,000-$99,999 and Householder Age 15-24 (Esri)
A15NW100CY 	 2018 Net Worth $100,000-$149,999 and Householder Age 15-24 (Esri)
A15NW150CY 	 2018 Net Worth $150,000-$249,999 and Householder Age 15-24 (Esri)
A15NW250CY 	 2018 Net Worth $250,000 or greater and Householder Age 15-24 (Esri)
AGGNWA15CY 	 2018 Aggregate Net Worth: Householder 15-24
MEDNWA15CY 	 2018 Median Net Worth and Householder Age 15-24 (Esri)
AVGNWA15CY 	 2018 Average Net Worth and Householder Age 15-24 (Esri)
A25NW0_CY 	 2018 Net Worth less than $15,000 and Householder Age 25-34 (Esri)
A25NW15_CY 	 2018 Net Worth $15,000-$34,999 and Householder Age 25-34 (Esri)
A25NW35_CY 	 2018 Net Worth $35,000-$49,999 and Householder Age 25-34 (Esri)
A25NW50_CY 	 2018 Net Worth $50,000-$99,999 and Householder Age 25-34 (Esri)
A25NW100CY 	 2018 Net Worth $100,000-$149,999 and Householder Age 25-34 (Esri)
A25NW150CY 	 2018 Net Worth $150,000-$249,999 and Householder Age 25-34 (Esri)
A25NW250CY 	 2018 Net Worth $250,000 or greater and Householder Age 25-34 (Esri)
AGGNWA25CY 	 2018 Aggregate Net Worth: Householder 25-34
MEDNWA25CY 	 2018 Median Net Worth and Householder Age 25-34 (Esri)
AVGNWA25CY 	 2018 Average Net Worth and Householder Age 25-34 (Esri)
A35NW0_CY 	 2018 Net Worth less than $15,000 and Householder Age 35-44 (Esri)
A35NW15_CY 	 2018 Net Worth $15,000-$34,999 and Householder Age 35-44 (Esri)
A35NW35_CY 	 2018 Net Worth $35,000-$49,999 and Householder Age 35-44 (Esri)
A35NW50_CY 	 2018 Net Worth $50,000-$99,999 and Householder Age 35-44 (Esri)
A35NW100CY 	 2018 Net Worth $100,000-$149,999 and Householder Age 35-44 (Esri)
A35NW150CY 	 2018 Net Worth $150,000-$249,999 and Householder Age 35-44 (Esri)
A35NW250CY 	 2018 Net Worth $250,000 or greater and Householder Age 35-44 (Esri)
AGGNWA35CY 	 2018 Aggregate Net Worth: Householder 35-44
MEDNWA35CY 	 2018 Median Net Worth and Householder Age 35-44 (Esri)
AVGNWA35CY 	 2018 Average Net Worth and Householder Age 35-44 (Esri)
A45NW0_CY 	 2018 Net Worth less than $15,000 and Householder Age 45-54 (Esri)
A45NW15_CY 	 2018 Net Worth $15,000-$34,999 and Householder Age 45-54 (Esri)
A45NW35_CY 	 2018 Net Worth $35,000-$49,999 and Householder Age 45-54 (Esri)
A45NW50_CY 	 2018 Net Worth $50,000-$99,999 and Householder Age 45-54 (Esri)
A45NW100CY 	 2018 Net Worth $100,000-$149,999 and Householder Age 45-54 (Esri)
A45NW150CY 	 2018 Net Worth $150,000-$249,999 and Householder Age 45-54 (Esri)
A45NW250CY 	 2018 Net Worth $250,000 or greater and Householder Age 45-54 (Esri)
AGGNWA45CY 	 2018 Aggregate Net Worth: Householder 45-54
MEDNWA45CY 	 2018 Median Net Worth and Householder Age 45-54 (Esri)
AVGNWA45CY 	 2018 Average Net Worth and Householder Age 45-54 (Esri)
A55NW0_CY 	 2018 Net Worth less than $15,000 and Householder Age 55-64 (Esri)
A55NW15_CY 	 2018 Net Worth $15,000-$34,999 and Householder Age 55-64 (Esri)
A55NW35_CY 	 2018 Net Worth $35,000-$49,999 and Householder Age 55-64 (Esri)
A55NW50_CY 	 2018 Net Worth $50,000-$99,999 and Householder Age 55-64 (Esri)
A55NW100CY 	 2018 Net Worth $100,000-$149,999 and Householder Age 55-64 (Esri)
A55NW150CY 	 2018 Net Worth $150,000-$249,999 and Householder Age 55-64 (Esri)
A55NW250CY 	 2018 Net Worth $250,000 or greater and Householder Age 55-64 (Esri)
AGGNWA55CY 	 2018 Aggregate Net Worth: Householder 55-64
MEDNWA55CY 	 2018 Median Net Worth and Householder Age 55-64 (Esri)
AVGNWA55CY 	 2018 Average Net Worth and Householder Age 55-64 (Esri)
A65NW0_CY 	 2018 Net Worth less than $15,000 and Householder Age 65-74 (Esri)
A65NW15_CY 	 2018 Net Worth $15,000-$34,999 and Householder Age 65-74 (Esri)
A65NW35_CY 	 2018 Net Worth $35,000-$49,999 and Householder Age 65-74 (Esri)
A65NW50_CY 	 2018 Net Worth $50,000-$99,999 and Householder Age 65-74 (Esri)
A65NW100CY 	 2018 Net Worth $100,000-$149,999 and Householder Age 65-74 (Esri)
A65NW150CY 	 2018 Net Worth $150,000-$249,999 and Householder Age 65-74 (Esri)
A65NW250CY 	 2018 Net Worth $250,000 or greater and Householder Age 65-74 (Esri)
AGGNWA65CY 	 2018 Aggregate Net Worth: Householder 65-74
MEDNWA65CY 	 2018 Median Net Worth and Householder Age 65-74 (Esri)
AVGNWA65CY 	 2018 Average Net Worth and Householder Age 65-74 (Esri)
A75NW0_CY 	 2018 Net Worth less than $15,000 and Householder Age 75+ (Esri)
A75NW15_CY 	 2018 Net Worth $15,000-$34,999 and Householder Age 75+ (Esri)
A75NW35_CY 	 2018 Net Worth $35,000-$49,999 and Householder Age 75+ (Esri)
A75NW50_CY 	 2018 Net Worth $50,000-$99,999 and Householder Age 75+ (Esri)
A75NW100CY 	 2018 Net Worth $100,000-$149,999 and Householder Age 75+ (Esri)
A75NW150CY 	 2018 Net Worth $150,000-$249,999 and Householder Age 75+ (Esri)
A75NW250CY 	 2018 Net Worth $250,000 or greater and Householder Age 75+ (Esri)
AGGNWA75CY 	 2018 Aggregate Net Worth: Householder 75+
MEDNWA75CY 	 2018 Median Net Worth and Householder Age 75+ (Esri)
AVGNWA75CY 	 2018 Average Net Worth and Householder Age 75+ (Esri)
NWA15BASCY 	 2018 Households by Net Worth Base and Householder Age 15-24 (Esri)
NWA25BASCY 	 2018 Households by Net Worth Base and Householder Age 25-34 (Esri)
NWA35BASCY 	 2018 Households by Net Worth Base and Householder Age 35-44 (Esri)
NWA45BASCY 	 2018 Households by Net Worth Base and Householder Age 45-54 (Esri)
NWA55BASCY 	 2018 Households by Net Worth Base and Householder Age 55-64 (Esri)
NWA65BASCY 	 2018 Households by Net Worth Base and Householder Age 65-74 (Esri)
NWA75BASCY 	 2018 Households by Net Worth Base and Householder Age 75+ (Esri)
VAL0_CY 	 2018 Home Value less than $50,000 (Esri)
VAL50K_CY 	 2018 Home Value $50,000-$99,999 (Esri)
VAL100K_CY 	 2018 Home Value $100,000-$149,999 (Esri)
VAL150K_CY 	 2018 Home Value $150,000-$199,999 (Esri)
VAL200K_CY 	 2018 Home Value $200,000-$249,999 (Esri)
VAL250K_CY 	 2018 Home Value $250,000-$299,999 (Esri)
VAL300K_CY 	 2018 Home Value $300,000-$399,999 (Esri)
VAL400K_CY 	 2018 Home Value $400,000-$499,999 (Esri)
VAL500K_CY 	 2018 Home Value $500,000-$749,999 (Esri)
VAL750K_CY 	 2018 Home Value $750,000-$999,999 (Esri)
VAL1M_CY 	 2018 Home Value $1,000,000-$1,499,999 (Esri)
VAL1PT5MCY 	 2018 Home Value $1,500,000-$1,999,999 (Esri)
VAL2M_CY 	 2018 Home Value $2,000,000 or greater (Esri)
VALBASE_CY 	 2018 Owner Occupied Housing Units by Value Base (Esri)
MEDVAL_CY 	 2018 Median Home Value (Esri)
AVGVAL_CY 	 2018 Average Home Value (Esri)
LANDAREA 	 Land Area in Square Miles
TOTPOP_FY 	 2023 Total Population (Esri)
HHPOP_FY 	 2023 Household Population (Esri)
FAMPOP_FY 	 2023 Family Population (Esri)
GQPOP_FY 	 2023 Group Quarters Population (Esri)
POPDENS_FY 	 2023 Population Density (Pop per Square Mile) (Esri)
TOTHH_FY 	 2023 Total Households (Esri)
AVGHHSZ_FY 	 2023 Average Household Size (Esri)
FAMHH_FY 	 2023 Total Family Households (Esri)
AVGFMSZ_FY 	 2023 Average Family Size (Esri)
TOTHU_FY 	 2023 Total Housing Units (Esri)
OWNER_FY 	 2023 Owner Occupied Housing Units (Esri)
RENTER_FY 	 2023 Renter Occupied Housing Units (Esri)
VACANT_FY 	 2023 Vacant Housing Units (Esri)
POPGRWCYFY 	 2018-2023 Population: Annual Growth Rate (Esri)
HHGRWCYFY 	 2018-2023 Households: Annual Growth Rate (Esri)
FAMGRWCYFY 	 2018-2023 Families: Annual Growth Rate (Esri)
PCIGRWCYFY 	 2018-2023 Per Capita Income: Annual Growth Rate (Esri)
OWNGRWCYFY 	 2018-2023 Owner Occupied Housing Units Annual Compound Growth Rate (Esri)
MHIGRWCYFY 	 2018-2023 Median Household Income: Annual Growth Rate (Esri)
GENALPHAFY 	 2023 Generation Alpha Population (Born 2017 or Later)
GENZ_FY 	 2023 Generation Z Population (Born 1999 to 2016)
MILLENN_FY 	 2023 Millennial Population (Born 1981 to 1998)
GENX_FY 	 2023 Generation X Population (Born 1965 to 1980)
BABYBOOMFY 	 2023 Baby Boomer Population (Born 1946 to 1964)
OLDRGENSFY 	 2023 Silent & Greatest Generations Population (Born 1945/Earlier)
GENBASE_FY 	 2023 Population by Generation Base
POP0_FY 	 2023 Total Population Age 0-4 (Esri)
POP5_FY 	 2023 Total Population Age 5-9 (Esri)
POP10_FY 	 2023 Total Population Age 10-14 (Esri)
POP15_FY 	 2023 Total Population Age 15-19 (Esri)
POP20_FY 	 2023 Total Population Age 20-24 (Esri)
POP25_FY 	 2023 Total Population Age 25-29 (Esri)
POP30_FY 	 2023 Total Population Age 30-34 (Esri)
POP35_FY 	 2023 Total Population Age 35-39 (Esri)
POP40_FY 	 2023 Total Population Age 40-44 (Esri)
POP45_FY 	 2023 Total Population Age 45-49 (Esri)
POP50_FY 	 2023 Total Population Age 50-54 (Esri)
POP55_FY 	 2023 Total Population Age 55-59 (Esri)
POP60_FY 	 2023 Total Population Age 60-64 (Esri)
POP65_FY 	 2023 Total Population Age 65-69 (Esri)
POP70_FY 	 2023 Total Population Age 70-74 (Esri)
POP75_FY 	 2023 Total Population Age 75-79 (Esri)
POP80_FY 	 2023 Total Population Age 80-84 (Esri)
POP85_FY 	 2023 Total Population Age 85+ (Esri)
POP18UP_FY 	 2023 Total Population Age 18+ (Esri)
POP21UP_FY 	 2023 Total Population Age 21+ (Esri)
MEDAGE_FY 	 2023 Median Age (Esri)
MALES_FY 	 2023 Male Population (Esri)
MALE0_FY 	 2023 Male Population Age 0-4 (Esri)
MALE5_FY 	 2023 Male Population Age 5-9 (Esri)
MALE10_FY 	 2023 Male Population Age 10-14 (Esri)
MALE15_FY 	 2023 Male Population Age 15-19 (Esri)
MALE20_FY 	 2023 Male Population Age 20-24 (Esri)
MALE25_FY 	 2023 Male Population Age 25-29 (Esri)
MALE30_FY 	 2023 Male Population Age 30-34 (Esri)
MALE35_FY 	 2023 Male Population Age 35-39 (Esri)
MALE40_FY 	 2023 Male Population Age 40-44 (Esri)
MALE45_FY 	 2023 Male Population Age 45-49 (Esri)
MALE50_FY 	 2023 Male Population Age 50-54 (Esri)
MALE55_FY 	 2023 Male Population Age 55-59 (Esri)
MALE60_FY 	 2023 Male Population Age 60-64 (Esri)
MALE65_FY 	 2023 Male Population Age 65-69 (Esri)
MALE70_FY 	 2023 Male Population Age 70-74 (Esri)
MALE75_FY 	 2023 Male Population Age 75-79 (Esri)
MALE80_FY 	 2023 Male Population Age 80-84 (Esri)
MALE85_FY 	 2023 Male Population Age 85+ (Esri)
MAL18UP_FY 	 2023 Male Population Age 18+ (Esri)
MAL21UP_FY 	 2023 Male Population Age 21+ (Esri)
MEDMAGE_FY 	 2023 Median Male Age (Esri)
FEMALES_FY 	 2023 Female Population (Esri)
FEM0_FY 	 2023 Female Population Age 0-4 (Esri)
FEM5_FY 	 2023 Female Population Age 5-9 (Esri)
FEM10_FY 	 2023 Female Population Age 10-14 (Esri)
FEM15_FY 	 2023 Female Population Age 15-19 (Esri)
FEM20_FY 	 2023 Female Population Age 20-24 (Esri)
FEM25_FY 	 2023 Female Population Age 25-29 (Esri)
FEM30_FY 	 2023 Female Population Age 30-34 (Esri)
FEM35_FY 	 2023 Female Population Age 35-39 (Esri)
FEM40_FY 	 2023 Female Population Age 40-44 (Esri)
FEM45_FY 	 2023 Female Population Age 45-49 (Esri)
FEM50_FY 	 2023 Female Population Age 50-54 (Esri)
FEM55_FY 	 2023 Female Population Age 55-59 (Esri)
FEM60_FY 	 2023 Female Population Age 60-64 (Esri)
FEM65_FY 	 2023 Female Population Age 65-69 (Esri)
FEM70_FY 	 2023 Female Population Age 70-74 (Esri)
FEM75_FY 	 2023 Female Population Age 75-79 (Esri)
FEM80_FY 	 2023 Female Population Age 80-84 (Esri)
FEM85_FY 	 2023 Female Population Age 85+ (Esri)
FEM18UP_FY 	 2023 Female Population Age 18+ (Esri)
FEM21UP_FY 	 2023 Female Population Age 21+ (Esri)
MEDFAGE_FY 	 2023 Median Female Age (Esri)
AGEBASE_FY 	 2023 Total Population by Five-Year Age Base (Esri)
AGE0_FY 	 2023 Total Population Age <1 (Esri)
AGE1_FY 	 2023 Total Population Age 1 (Esri)
AGE2_FY 	 2023 Total Population Age 2 (Esri)
AGE3_FY 	 2023 Total Population Age 3 (Esri)
AGE4_FY 	 2023 Total Population Age 4 (Esri)
AGE5_FY 	 2023 Total Population Age 5 (Esri)
AGE6_FY 	 2023 Total Population Age 6 (Esri)
AGE7_FY 	 2023 Total Population Age 7 (Esri)
AGE8_FY 	 2023 Total Population Age 8 (Esri)
AGE9_FY 	 2023 Total Population Age 9 (Esri)
AGE10_FY 	 2023 Total Population Age 10 (Esri)
AGE11_FY 	 2023 Total Population Age 11 (Esri)
AGE12_FY 	 2023 Total Population Age 12 (Esri)
AGE13_FY 	 2023 Total Population Age 13 (Esri)
AGE14_FY 	 2023 Total Population Age 14 (Esri)
AGE15_FY 	 2023 Total Population Age 15 (Esri)
AGE16_FY 	 2023 Total Population Age 16 (Esri)
AGE17_FY 	 2023 Total Population Age 17 (Esri)
AGE18_FY 	 2023 Total Population Age 18 (Esri)
AGE19_FY 	 2023 Total Population Age 19 (Esri)
AGE20_FY 	 2023 Total Population Age 20 (Esri)
AGE21_FY 	 2023 Total Population Age 21 (Esri)
AGE22_FY 	 2023 Total Population Age 22 (Esri)
AGE23_FY 	 2023 Total Population Age 23 (Esri)
AGE24_FY 	 2023 Total Population Age 24 (Esri)
AGE25_FY 	 2023 Total Population Age 25 (Esri)
AGE26_FY 	 2023 Total Population Age 26 (Esri)
AGE27_FY 	 2023 Total Population Age 27 (Esri)
AGE28_FY 	 2023 Total Population Age 28 (Esri)
AGE29_FY 	 2023 Total Population Age 29 (Esri)
AGE30_FY 	 2023 Total Population Age 30 (Esri)
AGE31_FY 	 2023 Total Population Age 31 (Esri)
AGE32_FY 	 2023 Total Population Age 32 (Esri)
AGE33_FY 	 2023 Total Population Age 33 (Esri)
AGE34_FY 	 2023 Total Population Age 34 (Esri)
AGE35_FY 	 2023 Total Population Age 35 (Esri)
AGE36_FY 	 2023 Total Population Age 36 (Esri)
AGE37_FY 	 2023 Total Population Age 37 (Esri)
AGE38_FY 	 2023 Total Population Age 38 (Esri)
AGE39_FY 	 2023 Total Population Age 39 (Esri)
AGE40_FY 	 2023 Total Population Age 40 (Esri)
AGE41_FY 	 2023 Total Population Age 41 (Esri)
AGE42_FY 	 2023 Total Population Age 42 (Esri)
AGE43_FY 	 2023 Total Population Age 43 (Esri)
AGE44_FY 	 2023 Total Population Age 44 (Esri)
AGE45_FY 	 2023 Total Population Age 45 (Esri)
AGE46_FY 	 2023 Total Population Age 46 (Esri)
AGE47_FY 	 2023 Total Population Age 47 (Esri)
AGE48_FY 	 2023 Total Population Age 48 (Esri)
AGE49_FY 	 2023 Total Population Age 49 (Esri)
AGE50_FY 	 2023 Total Population Age 50 (Esri)
AGE51_FY 	 2023 Total Population Age 51 (Esri)
AGE52_FY 	 2023 Total Population Age 52 (Esri)
AGE53_FY 	 2023 Total Population Age 53 (Esri)
AGE54_FY 	 2023 Total Population Age 54 (Esri)
AGE55_FY 	 2023 Total Population Age 55 (Esri)
AGE56_FY 	 2023 Total Population Age 56 (Esri)
AGE57_FY 	 2023 Total Population Age 57 (Esri)
AGE58_FY 	 2023 Total Population Age 58 (Esri)
AGE59_FY 	 2023 Total Population Age 59 (Esri)
AGE60_FY 	 2023 Total Population Age 60 (Esri)
AGE61_FY 	 2023 Total Population Age 61 (Esri)
AGE62_FY 	 2023 Total Population Age 62 (Esri)
AGE63_FY 	 2023 Total Population Age 63 (Esri)
AGE64_FY 	 2023 Total Population Age 64 (Esri)
AGE65_FY 	 2023 Total Population Age 65 (Esri)
AGE66_FY 	 2023 Total Population Age 66 (Esri)
AGE67_FY 	 2023 Total Population Age 67 (Esri)
AGE68_FY 	 2023 Total Population Age 68 (Esri)
AGE69_FY 	 2023 Total Population Age 69 (Esri)
AGE70_FY 	 2023 Total Population Age 70 (Esri)
AGE71_FY 	 2023 Total Population Age 71 (Esri)
AGE72_FY 	 2023 Total Population Age 72 (Esri)
AGE73_FY 	 2023 Total Population Age 73 (Esri)
AGE74_FY 	 2023 Total Population Age 74 (Esri)
AGE75_FY 	 2023 Total Population Age 75 (Esri)
AGE76_FY 	 2023 Total Population Age 76 (Esri)
AGE77_FY 	 2023 Total Population Age 77 (Esri)
AGE78_FY 	 2023 Total Population Age 78 (Esri)
AGE79_FY 	 2023 Total Population Age 79 (Esri)
AGE80_FY 	 2023 Total Population Age 80 (Esri)
AGE81_FY 	 2023 Total Population Age 81 (Esri)
AGE82_FY 	 2023 Total Population Age 82 (Esri)
AGE83_FY 	 2023 Total Population Age 83 (Esri)
AGE84_FY 	 2023 Total Population Age 84 (Esri)
MAGE0_FY 	 2023 Male Population Age <1 (Esri)
MAGE1_FY 	 2023 Male Population Age 1 (Esri)
MAGE2_FY 	 2023 Male Population Age 2 (Esri)
MAGE3_FY 	 2023 Male Population Age 3 (Esri)
MAGE4_FY 	 2023 Male Population Age 4 (Esri)
MAGE5_FY 	 2023 Male Population Age 5 (Esri)
MAGE6_FY 	 2023 Male Population Age 6 (Esri)
MAGE7_FY 	 2023 Male Population Age 7 (Esri)
MAGE8_FY 	 2023 Male Population Age 8 (Esri)
MAGE9_FY 	 2023 Male Population Age 9 (Esri)
MAGE10_FY 	 2023 Male Population Age 10 (Esri)
MAGE11_FY 	 2023 Male Population Age 11 (Esri)
MAGE12_FY 	 2023 Male Population Age 12 (Esri)
MAGE13_FY 	 2023 Male Population Age 13 (Esri)
MAGE14_FY 	 2023 Male Population Age 14 (Esri)
MAGE15_FY 	 2023 Male Population Age 15 (Esri)
MAGE16_FY 	 2023 Male Population Age 16 (Esri)
MAGE17_FY 	 2023 Male Population Age 17 (Esri)
MAGE18_FY 	 2023 Male Population Age 18 (Esri)
MAGE19_FY 	 2023 Male Population Age 19 (Esri)
MAGE20_FY 	 2023 Male Population Age 20 (Esri)
MAGE21_FY 	 2023 Male Population Age 21 (Esri)
MAGE22_FY 	 2023 Male Population Age 22 (Esri)
MAGE23_FY 	 2023 Male Population Age 23 (Esri)
MAGE24_FY 	 2023 Male Population Age 24 (Esri)
MAGE25_FY 	 2023 Male Population Age 25 (Esri)
MAGE26_FY 	 2023 Male Population Age 26 (Esri)
MAGE27_FY 	 2023 Male Population Age 27 (Esri)
MAGE28_FY 	 2023 Male Population Age 28 (Esri)
MAGE29_FY 	 2023 Male Population Age 29 (Esri)
MAGE30_FY 	 2023 Male Population Age 30 (Esri)
MAGE31_FY 	 2023 Male Population Age 31 (Esri)
MAGE32_FY 	 2023 Male Population Age 32 (Esri)
MAGE33_FY 	 2023 Male Population Age 33 (Esri)
MAGE34_FY 	 2023 Male Population Age 34 (Esri)
MAGE35_FY 	 2023 Male Population Age 35 (Esri)
MAGE36_FY 	 2023 Male Population Age 36 (Esri)
MAGE37_FY 	 2023 Male Population Age 37 (Esri)
MAGE38_FY 	 2023 Male Population Age 38 (Esri)
MAGE39_FY 	 2023 Male Population Age 39 (Esri)
MAGE40_FY 	 2023 Male Population Age 40 (Esri)
MAGE41_FY 	 2023 Male Population Age 41 (Esri)
MAGE42_FY 	 2023 Male Population Age 42 (Esri)
MAGE43_FY 	 2023 Male Population Age 43 (Esri)
MAGE44_FY 	 2023 Male Population Age 44 (Esri)
MAGE45_FY 	 2023 Male Population Age 45 (Esri)
MAGE46_FY 	 2023 Male Population Age 46 (Esri)
MAGE47_FY 	 2023 Male Population Age 47 (Esri)
MAGE48_FY 	 2023 Male Population Age 48 (Esri)
MAGE49_FY 	 2023 Male Population Age 49 (Esri)
MAGE50_FY 	 2023 Male Population Age 50 (Esri)
MAGE51_FY 	 2023 Male Population Age 51 (Esri)
MAGE52_FY 	 2023 Male Population Age 52 (Esri)
MAGE53_FY 	 2023 Male Population Age 53 (Esri)
MAGE54_FY 	 2023 Male Population Age 54 (Esri)
MAGE55_FY 	 2023 Male Population Age 55 (Esri)
MAGE56_FY 	 2023 Male Population Age 56 (Esri)
MAGE57_FY 	 2023 Male Population Age 57 (Esri)
MAGE58_FY 	 2023 Male Population Age 58 (Esri)
MAGE59_FY 	 2023 Male Population Age 59 (Esri)
MAGE60_FY 	 2023 Male Population Age 60 (Esri)
MAGE61_FY 	 2023 Male Population Age 61 (Esri)
MAGE62_FY 	 2023 Male Population Age 62 (Esri)
MAGE63_FY 	 2023 Male Population Age 63 (Esri)
MAGE64_FY 	 2023 Male Population Age 64 (Esri)
MAGE65_FY 	 2023 Male Population Age 65 (Esri)
MAGE66_FY 	 2023 Male Population Age 66 (Esri)
MAGE67_FY 	 2023 Male Population Age 67 (Esri)
MAGE68_FY 	 2023 Male Population Age 68 (Esri)
MAGE69_FY 	 2023 Male Population Age 69 (Esri)
MAGE70_FY 	 2023 Male Population Age 70 (Esri)
MAGE71_FY 	 2023 Male Population Age 71 (Esri)
MAGE72_FY 	 2023 Male Population Age 72 (Esri)
MAGE73_FY 	 2023 Male Population Age 73 (Esri)
MAGE74_FY 	 2023 Male Population Age 74 (Esri)
MAGE75_FY 	 2023 Male Population Age 75 (Esri)
MAGE76_FY 	 2023 Male Population Age 76 (Esri)
MAGE77_FY 	 2023 Male Population Age 77 (Esri)
MAGE78_FY 	 2023 Male Population Age 78 (Esri)
MAGE79_FY 	 2023 Male Population Age 79 (Esri)
MAGE80_FY 	 2023 Male Population Age 80 (Esri)
MAGE81_FY 	 2023 Male Population Age 81 (Esri)
MAGE82_FY 	 2023 Male Population Age 82 (Esri)
MAGE83_FY 	 2023 Male Population Age 83 (Esri)
MAGE84_FY 	 2023 Male Population Age 84 (Esri)
FAGE0_FY 	 2023 Female Population Age <1 (Esri)
FAGE1_FY 	 2023 Female Population Age 1 (Esri)
FAGE2_FY 	 2023 Female Population Age 2 (Esri)
FAGE3_FY 	 2023 Female Population Age 3 (Esri)
FAGE4_FY 	 2023 Female Population Age 4 (Esri)
FAGE5_FY 	 2023 Female Population Age 5 (Esri)
FAGE6_FY 	 2023 Female Population Age 6 (Esri)
FAGE7_FY 	 2023 Female Population Age 7 (Esri)
FAGE8_FY 	 2023 Female Population Age 8 (Esri)
FAGE9_FY 	 2023 Female Population Age 9 (Esri)
FAGE10_FY 	 2023 Female Population Age 10 (Esri)
FAGE11_FY 	 2023 Female Population Age 11 (Esri)
FAGE12_FY 	 2023 Female Population Age 12 (Esri)
FAGE13_FY 	 2023 Female Population Age 13 (Esri)
FAGE14_FY 	 2023 Female Population Age 14 (Esri)
FAGE15_FY 	 2023 Female Population Age 15 (Esri)
FAGE16_FY 	 2023 Female Population Age 16 (Esri)
FAGE17_FY 	 2023 Female Population Age 17 (Esri)
FAGE18_FY 	 2023 Female Population Age 18 (Esri)
FAGE19_FY 	 2023 Female Population Age 19 (Esri)
FAGE20_FY 	 2023 Female Population Age 20 (Esri)
FAGE21_FY 	 2023 Female Population Age 21 (Esri)
FAGE22_FY 	 2023 Female Population Age 22 (Esri)
FAGE23_FY 	 2023 Female Population Age 23 (Esri)
FAGE24_FY 	 2023 Female Population Age 24 (Esri)
FAGE25_FY 	 2023 Female Population Age 25 (Esri)
FAGE26_FY 	 2023 Female Population Age 26 (Esri)
FAGE27_FY 	 2023 Female Population Age 27 (Esri)
FAGE28_FY 	 2023 Female Population Age 28 (Esri)
FAGE29_FY 	 2023 Female Population Age 29 (Esri)
FAGE30_FY 	 2023 Female Population Age 30 (Esri)
FAGE31_FY 	 2023 Female Population Age 31 (Esri)
FAGE32_FY 	 2023 Female Population Age 32 (Esri)
FAGE33_FY 	 2023 Female Population Age 33 (Esri)
FAGE34_FY 	 2023 Female Population Age 34 (Esri)
FAGE35_FY 	 2023 Female Population Age 35 (Esri)
FAGE36_FY 	 2023 Female Population Age 36 (Esri)
FAGE37_FY 	 2023 Female Population Age 37 (Esri)
FAGE38_FY 	 2023 Female Population Age 38 (Esri)
FAGE39_FY 	 2023 Female Population Age 39 (Esri)
FAGE40_FY 	 2023 Female Population Age 40 (Esri)
FAGE41_FY 	 2023 Female Population Age 41 (Esri)
FAGE42_FY 	 2023 Female Population Age 42 (Esri)
FAGE43_FY 	 2023 Female Population Age 43 (Esri)
FAGE44_FY 	 2023 Female Population Age 44 (Esri)
FAGE45_FY 	 2023 Female Population Age 45 (Esri)
FAGE46_FY 	 2023 Female Population Age 46 (Esri)
FAGE47_FY 	 2023 Female Population Age 47 (Esri)
FAGE48_FY 	 2023 Female Population Age 48 (Esri)
FAGE49_FY 	 2023 Female Population Age 49 (Esri)
FAGE50_FY 	 2023 Female Population Age 50 (Esri)
FAGE51_FY 	 2023 Female Population Age 51 (Esri)
FAGE52_FY 	 2023 Female Population Age 52 (Esri)
FAGE53_FY 	 2023 Female Population Age 53 (Esri)
FAGE54_FY 	 2023 Female Population Age 54 (Esri)
FAGE55_FY 	 2023 Female Population Age 55 (Esri)
FAGE56_FY 	 2023 Female Population Age 56 (Esri)
FAGE57_FY 	 2023 Female Population Age 57 (Esri)
FAGE58_FY 	 2023 Female Population Age 58 (Esri)
FAGE59_FY 	 2023 Female Population Age 59 (Esri)
FAGE60_FY 	 2023 Female Population Age 60 (Esri)
FAGE61_FY 	 2023 Female Population Age 61 (Esri)
FAGE62_FY 	 2023 Female Population Age 62 (Esri)
FAGE63_FY 	 2023 Female Population Age 63 (Esri)
FAGE64_FY 	 2023 Female Population Age 64 (Esri)
FAGE65_FY 	 2023 Female Population Age 65 (Esri)
FAGE66_FY 	 2023 Female Population Age 66 (Esri)
FAGE67_FY 	 2023 Female Population Age 67 (Esri)
FAGE68_FY 	 2023 Female Population Age 68 (Esri)
FAGE69_FY 	 2023 Female Population Age 69 (Esri)
FAGE70_FY 	 2023 Female Population Age 70 (Esri)
FAGE71_FY 	 2023 Female Population Age 71 (Esri)
FAGE72_FY 	 2023 Female Population Age 72 (Esri)
FAGE73_FY 	 2023 Female Population Age 73 (Esri)
FAGE74_FY 	 2023 Female Population Age 74 (Esri)
FAGE75_FY 	 2023 Female Population Age 75 (Esri)
FAGE76_FY 	 2023 Female Population Age 76 (Esri)
FAGE77_FY 	 2023 Female Population Age 77 (Esri)
FAGE78_FY 	 2023 Female Population Age 78 (Esri)
FAGE79_FY 	 2023 Female Population Age 79 (Esri)
FAGE80_FY 	 2023 Female Population Age 80 (Esri)
FAGE81_FY 	 2023 Female Population Age 81 (Esri)
FAGE82_FY 	 2023 Female Population Age 82 (Esri)
FAGE83_FY 	 2023 Female Population Age 83 (Esri)
FAGE84_FY 	 2023 Female Population Age 84 (Esri)
WHITE_FY 	 2023 White Population (Esri)
BLACK_FY 	 2023 Black/African American Population (Esri)
AMERIND_FY 	 2023 American Indian/Alaska Native Population (Esri)
ASIAN_FY 	 2023 Asian Population (Esri)
PACIFIC_FY 	 2023 Pacific Islander Population (Esri)
OTHRACE_FY 	 2023 Other Race Population (Esri)
RACE2UP_FY 	 2023 Population of Two or More Races (Esri)
HISPPOP_FY 	 2023 Hispanic Population (Esri)
HISPWHT_FY 	 2023 Hispanic White Population (Esri)
HISPBLK_FY 	 2023 Hispanic Black/African American Population (Esri)
HISPAI_FY 	 2023 Hispanic American Indian/Alaska Native Population (Esri)
HISPASN_FY 	 2023 Hispanic Asian Population (Esri)
HISPPI_FY 	 2023 Hispanic Pacific Islander Population (Esri)
HISPOTH_FY 	 2023 Hispanic Other Race Population (Esri)
HISPMLT_FY 	 2023 Hispanic Population of Two or More Races (Esri)
NONHISP_FY 	 2023 Non-Hispanic Population (Esri)
NHSPWHT_FY 	 2023 White Non-Hispanic Population (Esri)
NHSPBLK_FY 	 2023 Black/African American Non-Hispanic Population (Esri)
NHSPAI_FY 	 2023 American Indian/Alaska Native Non-Hispanic Population (Esri)
NHSPASN_FY 	 2023 Asian Non-Hispanic Population (Esri)
NHSPPI_FY 	 2023 Pacific Islander Non-Hispanic Population (Esri)
NHSPOTH_FY 	 2023 Other Race Non-Hispanic Population (Esri)
NHSPMLT_FY 	 2023 Multiple Races Non-Hispanic Population (Esri)
MINORITYFY 	 2023 Minority Population (Esri)
DIVINDX_FY 	 2023 Diversity Index (Esri)
RACEBASEFY 	 2023 Population by Race Base (Esri)
HINC0_FY 	 2023 Household Income less than $15,000 (Esri)
HINC15_FY 	 2023 Household Income $15,000-$24,999 (Esri)
HINC25_FY 	 2023 Household Income $25,000-$34,999 (Esri)
HINC35_FY 	 2023 Household Income $35,000-$49,999 (Esri)
HINC50_FY 	 2023 Household Income $50,000-$74,999 (Esri)
HINC75_FY 	 2023 Household Income $75,000-$99,999 (Esri)
HINC100_FY 	 2023 Household Income $100,000-$149,999 (Esri)
HINC150_FY 	 2023 Household Income $150,000-$199,999 (Esri)
HINC200_FY 	 2023 Household Income $200,000 or greater (Esri)
MEDHINC_FY 	 2023 Median Household Income (Esri)
AVGHINC_FY 	 2023 Average Household Income (Esri)
PCI_FY 	 2023 Per Capita Income (Esri)
AGGINC_FY 	 2023 Aggregate Income (Esri)
AGGHINC_FY 	 2023 Aggregate Household Income (Esri)
HINCBASEFY 	 2023 Households by Income Base (Esri)
A15I0_FY 	 2023 Household Income less than $15,000 and Householder Age 15-24 (Esri)
A15I15_FY 	 2023 Household Income $15,000-$24,999 and Householder Age 15-24 (Esri)
A15I25_FY 	 2023 Household Income $25,000-$34,999 and Householder Age 15-24 (Esri)
A15I35_FY 	 2023 Household Income $35,000-$49,999 and Householder Age 15-24 (Esri)
A15I50_FY 	 2023 Household Income $50,000-$74,999 and Householder Age 15-24 (Esri)
A15I75_FY 	 2023 Household Income $75,000-$99,999 and Householder Age 15-24 (Esri)
A15I100_FY 	 2023 Household Income $100,000-$149,999 and Householder Age 15-24 (Esri)
A15I150_FY 	 2023 Household Income $150,000-$199,999 and Householder Age 15-24 (Esri)
A15I200_FY 	 2023 Household Income $200,000+ and Householder Age 15-24 (Esri)
MEDIA15_FY 	 2023 Median Household Income and Householder Age 15-24 (Esri)
AVGIA15_FY 	 2023 Average Household Income and Householder Age 15-24 (Esri)
IA15BASEFY 	 2023 Households by Income Base and Householder Age 15-24 (Esri)
AGGIA15_FY 	 2023 Aggregate Household Income and Householder Age 15-24 (Esri)
A25I0_FY 	 2023 Household Income less than $15,000 and Householder Age 25-34 (Esri)
A25I15_FY 	 2023 Household Income $15,000-$24,999 and Householder Age 25-34 (Esri)
A25I25_FY 	 2023 Household Income $25,000-$34,999 and Householder Age 25-34 (Esri)
A25I35_FY 	 2023 Household Income $35,000-$49,999 and Householder Age 25-34 (Esri)
A25I50_FY 	 2023 Household Income $50,000-$74,999 and Householder Age 25-34 (Esri)
A25I75_FY 	 2023 Household Income $75,000-$99,999 and Householder Age 25-34 (Esri)
A25I100_FY 	 2023 Household Income $100,000-$149,999 and Householder Age 25-34 (Esri)
A25I150_FY 	 2023 Household Income $150,000-$199,999 and Householder Age 25-34 (Esri)
A25I200_FY 	 2023 Household Income $200,000+ and Householder Age 25-34 (Esri)
MEDIA25_FY 	 2023 Median Household Income and Householder Age 25-34 (Esri)
AVGIA25_FY 	 2023 Average Household Income and Householder Age 25-34 (Esri)
IA25BASEFY 	 2023 Households by Income Base and Householder Age 25-34 (Esri)
AGGIA25_FY 	 2023 Aggregate Household Income and Householder Age 25-34 (Esri)
A35I0_FY 	 2023 Household Income less than $15,000 and Householder Age 35-44 (Esri)
A35I15_FY 	 2023 Household Income $15,000-$24,999 and Householder Age 35-44 (Esri)
A35I25_FY 	 2023 Household Income $25,000-$34,999 and Householder Age 35-44 (Esri)
A35I35_FY 	 2023 Household Income $35,000-$49,999 and Householder Age 35-44 (Esri)
A35I50_FY 	 2023 Household Income $50,000-$74,999 and Householder Age 35-44 (Esri)
A35I75_FY 	 2023 Household Income $75,000-$99,999 and Householder Age 35-44 (Esri)
A35I100_FY 	 2023 Household Income $100,000-$149,999 and Householder Age 35-44 (Esri)
A35I150_FY 	 2023 Household Income $150,000-$199,999 and Householder Age 35-44 (Esri)
A35I200_FY 	 2023 Household Income $200,000+ and Householder Age 35-44 (Esri)
MEDIA35_FY 	 2023 Median Household Income and Householder Age 35-44 (Esri)
AVGIA35_FY 	 2023 Average Household Income and Householder Age 35-44 (Esri)
IA35BASEFY 	 2023 Households by Income Base and Householder Age 35-44 (Esri)
AGGIA35_FY 	 2023 Aggregate Household Income and Householder Age 35-44 (Esri)
A45I0_FY 	 2023 Household Income less than $15,000 and Householder Age 45-54 (Esri)
A45I15_FY 	 2023 Household Income $15,000-$24,999 and Householder Age 45-54 (Esri)
A45I25_FY 	 2023 Household Income $25,000-$34,999 and Householder Age 45-54 (Esri)
A45I35_FY 	 2023 Household Income $35,000-$49,999 and Householder Age 45-54 (Esri)
A45I50_FY 	 2023 Household Income $50,000-$74,999 and Householder Age 45-54 (Esri)
A45I75_FY 	 2023 Household Income $75,000-$99,999 and Householder Age 45-54 (Esri)
A45I100_FY 	 2023 Household Income $100,000-$149,999 and Householder Age 45-54 (Esri)
A45I150_FY 	 2023 Household Income $150,000-$199,999 and Householder Age 45-54 (Esri)
A45I200_FY 	 2023 Household Income $200,000+ and Householder Age 45-54 (Esri)
MEDIA45_FY 	 2023 Median Household Income and Householder Age 45-54 (Esri)
AVGIA45_FY 	 2023 Average Household Income and Householder Age 45-54 (Esri)
IA45BASEFY 	 2023 Households by Income Base and Householder Age 45-54 (Esri)
AGGIA45_FY 	 2023 Aggregate Household Income and Householder Age 45-54 (Esri)
A55I0_FY 	 2023 Household Income less than $15,000 and Householder Age 55-64 (Esri)
A55I15_FY 	 2023 Household Income $15,000-$24,999 and Householder Age 55-64 (Esri)
A55I25_FY 	 2023 Household Income $25,000-$34,999 and Householder Age 55-64 (Esri)
A55I35_FY 	 2023 Household Income $35,000-$49,999 and Householder Age 55-64 (Esri)
A55I50_FY 	 2023 Household Income $50,000-$74,999 and Householder Age 55-64 (Esri)
A55I75_FY 	 2023 Household Income $75,000-$99,999 and Householder Age 55-64 (Esri)
A55I100_FY 	 2023 Household Income $100,000-$149,999 and Householder Age 55-64 (Esri)
A55I150_FY 	 2023 Household Income $150,000-$199,999 and Householder Age 55-64 (Esri)
A55I200_FY 	 2023 Household Income $200,000+ and Householder Age 55-64 (Esri)
MEDIA55_FY 	 2023 Median Household Income and Householder Age 55-64 (Esri)
AVGIA55_FY 	 2023 Average Household Income and Householder Age 55-64 (Esri)
IA55BASEFY 	 2023 Households by Income Base and Householder Age 55-64 (Esri)
AGGIA55_FY 	 2023 Aggregate Household Income and Householder Age 55-64 (Esri)
A65I0_FY 	 2023 Household Income less than $15,000 and Householder Age 65-74 (Esri)
A65I15_FY 	 2023 Household Income $15,000-$24,999 and Householder Age 65-74 (Esri)
A65I25_FY 	 2023 Household Income $25,000-$34,999 and Householder Age 65-74 (Esri)
A65I35_FY 	 2023 Household Income $35,000-$49,999 and Householder Age 65-74 (Esri)
A65I50_FY 	 2023 Household Income $50,000-$74,999 and Householder Age 65-74 (Esri)
A65I75_FY 	 2023 Household Income $75,000-$99,999 and Householder Age 65-74 (Esri)
A65I100_FY 	 2023 Household Income $100,000-$149,999 and Householder Age 65-74 (Esri)
A65I150_FY 	 2023 Household Income $150,000-$199,999 and Householder Age 65-74 (Esri)
A65I200_FY 	 2023 Household Income $200,000+ and Householder Age 65-74 (Esri)
MEDIA65_FY 	 2023 Median Household Income and Householder Age 65-74 (Esri)
AVGIA65_FY 	 2023 Average Household Income and Householder Age 65-74 (Esri)
IA65BASEFY 	 2023 Households by Income Base and Householder Age 65-74 (Esri)
AGGIA65_FY 	 2023 Aggregate Household Income and Householder Age 65-74 (Esri)
A75I0_FY 	 2023 Household Income less than $15,000 and Householder Age 75+ (Esri)
A75I15_FY 	 2023 Household Income $15,000-$24,999 and Householder Age 75+ (Esri)
A75I25_FY 	 2023 Household Income $25,000-$34,999 and Householder Age 75+ (Esri)
A75I35_FY 	 2023 Household Income $35,000-$49,999 and Householder Age 75+ (Esri)
A75I50_FY 	 2023 Household Income $50,000-$74,999 and Householder Age 75+ (Esri)
A75I75_FY 	 2023 Household Income $75,000-$99,999 and Householder Age 75+ (Esri)
A75I100_FY 	 2023 Household Income $100,000-$149,999 and Householder Age 75+ (Esri)
A75I150_FY 	 2023 Household Income $150,000-$199,999 and Householder Age 75+ (Esri)
A75I200_FY 	 2023 Household Income $200,000+ and Householder Age 75+ (Esri)
MEDIA75_FY 	 2023 Median Household Income and Householder Age 75+ (Esri)
AVGIA75_FY 	 2023 Average Household Income and Householder Age 75+ (Esri)
IA75BASEFY 	 2023 Households by Income Base and Householder Age 75+ (Esri)
AGGIA75_FY 	 2023 Aggregate Household Income and Householder Age 75+ (Esri)
MEDHHR_FY 	 2023 Median Age of Householder (Esri)
MEDIA55UFY 	 2023 Median Household Income and Householder Age 55+ (Esri)
AVGIA55UFY 	 2023 Average Household Income and Householder Age 55+ (Esri)
IA55UBASFY 	 2023 Households by Income Base and Householder Age 55+ (Esri)
MEDIA65UFY 	 2023 Median Household Income and Householder Age 65+ (Esri)
AVGIA65UFY 	 2023 Average Household Income and Householder Age 65+ (Esri)
IA65UBASFY 	 2023 Households by Income Base and Householder Age 65+ (Esri)
VAL0_FY 	 2023 Home Value less than $50,000 (Esri)
VAL50K_FY 	 2023 Home Value $50,000-$99,999 (Esri)
VAL100K_FY 	 2023 Home Value $100,000-$149,999 (Esri)
VAL150K_FY 	 2023 Home Value $150,000-$199,999 (Esri)
VAL200K_FY 	 2023 Home Value $200,000-$249,999 (Esri)
VAL250K_FY 	 2023 Home Value $250,000-$299,999 (Esri)
VAL300K_FY 	 2023 Home Value $300,000-$399,999 (Esri)
VAL400K_FY 	 2023 Home Value $400,000-$499,999 (Esri)
VAL500K_FY 	 2023 Home Value $500,000-$749,999 (Esri)
VAL750K_FY 	 2023 Home Value $750,000-$999,999 (Esri)
VAL1M_FY 	 2023 Home Value $1,000,000-$1,499,999 (Esri)
VAL1PT5MFY 	 2023 Home Value $1,500,000-$1,999,999 (Esri)
VAL2M_FY 	 2023 Home Value $2,000,000 or greater (Esri)
MEDVAL_FY 	 2023 Median Home Value (Esri)
AVGVAL_FY 	 2023 Average Home Value (Esri)
VALBASE_FY 	 2023 Owner Occupied Housing Units by Value Base (Esri)
TOTPOP10 	 2010 Total Population (U.S. Census)
POPDENS10 	 2010 Population Density (Pop per Square Mile) (U.S. Census)
MALES10 	 2010 Male Population (U.S. Census)
FEMALES10 	 2010 Female Population (U.S. Census)
HHPOP10 	 2010 Population in Households (U.S. Census)
FAMPOP10 	 2010 Population in Families (U.S. Census)
POPGRW0010 	 2000-2010 Population Annual Compound Growth Rate (U.S. Census)
URBARPOP10 	 2010 Population Inside Urbanized Areas (U.S. Census)
URBCLPOP10 	 2010 Population Inside Urban Clusters (U.S. Census)
RURALPOP10 	 2010 Rural Population (U.S. Census)
URBANPOP10 	 2010 Urban Population (U.S. Census)
URPOPBAS10 	 2010 Urban/Rural Population Base (U.S. Census)
POP0C10 	 2010 Total Population Age 0-4 (U.S. Census)
POP5C10 	 2010 Total Population Age 5-9  (U.S. Census)
POP10C10 	 2010 Total Population Age 10-14  (U.S. Census)
POP15C10 	 2010 Total Population Age 15-19  (U.S. Census)
POP20C10 	 2010 Total Population Age 20-24  (U.S. Census)
POP25C10 	 2010 Total Population Age 25-29  (U.S. Census)
POP30C10 	 2010 Total Population Age 30-34  (U.S. Census)
POP35C10 	 2010 Total Population Age 35-39  (U.S. Census)
POP40C10 	 2010 Total Population Age 40-44  (U.S. Census)
POP45C10 	 2010 Total Population Age 45-49  (U.S. Census)
POP50C10 	 2010 Total Population Age 50-54  (U.S. Census)
POP55C10 	 2010 Total Population Age 55-59  (U.S. Census)
POP60C10 	 2010 Total Population Age 60-64  (U.S. Census)
POP65C10 	 2010 Total Population Age 65-69  (U.S. Census)
POP70C10 	 2010 Total Population Age 70-74  (U.S. Census)
POP75C10 	 2010 Total Population Age 75-79  (U.S. Census)
POP80C10 	 2010 Total Population Age 80-84  (U.S. Census)
POP85C10 	 2010 Total Population Age 85+ (U.S. Census)
ADULTS10 	 2010 Population Age 18+ (U.S. Census)
POP21UP10 	 2010 Total Population Age 21+ (U.S. Census)
MEDAGE10 	 2010 Median Age (U.S. Census)
AGEBASE10 	 2010 Base for Total Population by 5-Year Age Ranges (U.S. Census)
MALE0C10 	 2010 Males Age 0-4 (U.S. Census)
MALE5C10 	 2010 Males Age 5-9 (U.S. Census)
MALE10C10 	 2010 Males Age 10-14 (U.S. Census)
MALE15C10 	 2010 Males Age 15-19 (U.S. Census)
MALE20C10 	 2010 Males Age 20-24 (U.S. Census)
MALE25C10 	 2010 Males Age 25-29 (U.S. Census)
MALE30C10 	 2010 Males Age 30-34 (U.S. Census)
MALE35C10 	 2010 Males Age 35-39 (U.S. Census)
MALE40C10 	 2010 Males Age 40-44 (U.S. Census)
MALE45C10 	 2010 Males Age 45-49 (U.S. Census)
MALE50C10 	 2010 Males Age 50-54 (U.S. Census)
MALE55C10 	 2010 Males Age 55-59 (U.S. Census)
MALE60C10 	 2010 Males Age 60-64 (U.S. Census)
MALE65C10 	 2010 Males Age 65-69 (U.S. Census)
MALE70C10 	 2010 Males Age 70-74 (U.S. Census)
MALE75C10 	 2010 Males Age 75-79 (U.S. Census)
MALE80C10 	 2010 Males Age 80-84 (U.S. Census)
MALE85C10 	 2010 Males Age 85+ (U.S. Census)
MAL18UP10 	 2010 Males Age 18+ (U.S. Census)
MAL21UP10 	 2010 Males Age 21+ (U.S. Census)
MEDMAGE10 	 2010 Median Male Age (U.S. Census)
MAGEBASE10 	 2010 Base for Male Population by 5-Year Age Ranges (U.S. Census)
FEM0C10 	 2010 Females Age 0-4 (U.S. Census)
FEM5C10 	 2010 Females Age 5-9 (U.S. Census)
FEM10C10 	 2010 Females Age 10-14 (U.S. Census)
FEM15C10 	 2010 Females Age 15-19 (U.S. Census)
FEM20C10 	 2010 Females Age 20-24 (U.S. Census)
FEM25C10 	 2010 Females Age 25-29 (U.S. Census)
FEM30C10 	 2010 Females Age 30-34 (U.S. Census)
FEM35C10 	 2010 Females Age 35-39 (U.S. Census)
FEM40C10 	 2010 Females Age 40-44 (U.S. Census)
FEM45C10 	 2010 Females Age 45-49 (U.S. Census)
FEM50C10 	 2010 Females Age 50-54 (U.S. Census)
FEM55C10 	 2010 Females Age 55-59 (U.S. Census)
FEM60C10 	 2010 Females Age 60-64 (U.S. Census)
FEM65C10 	 2010 Females Age 65-69 (U.S. Census)
FEM70C10 	 2010 Females Age 70-74 (U.S. Census)
FEM75C10 	 2010 Females Age 75-79 (U.S. Census)
FEM80C10 	 2010 Females Age 80-84 (U.S. Census)
FEM85C10 	 2010 Females Age 85+ (U.S. Census)
FEM18UP10 	 2010 Females Age 18+ (U.S. Census)
FEM21UP10 	 2010 Females Age 21+ (U.S. Census)
MEDFAGE10 	 2010 Median Female Age (U.S. Census)
FAGEBASE10 	 2010 Base for Female Population by 5-Year Age Ranges (U.S. Census)
AGE0C10 	 2010 Total Population Age <1 (U.S. Census)
AGE1C10 	 2010 Total Population Age 1 (U.S. Census)
AGE2C10 	 2010 Total Population Age 2 (U.S. Census)
AGE3C10 	 2010 Total Population Age 3 (U.S. Census)
AGE4C10 	 2010 Total Population Age 4 (U.S. Census)
AGE5C10 	 2010 Total Population Age 5 (U.S. Census)
AGE6C10 	 2010 Total Population Age 6 (U.S. Census)
AGE7C10 	 2010 Total Population Age 7 (U.S. Census)
AGE8C10 	 2010 Total Population Age 8 (U.S. Census)
AGE9C10 	 2010 Total Population Age 9 (U.S. Census)
AGE10C10 	 2010 Total Population Age 10 (U.S. Census)
AGE11C10 	 2010 Total Population Age 11 (U.S. Census)
AGE12C10 	 2010 Total Population Age 12 (U.S. Census)
AGE13C10 	 2010 Total Population Age 13 (U.S. Census)
AGE14C10 	 2010 Total Population Age 14 (U.S. Census)
AGE15C10 	 2010 Total Population Age 15 (U.S. Census)
AGE16C10 	 2010 Total Population Age 16 (U.S. Census)
AGE17C10 	 2010 Total Population Age 17 (U.S. Census)
AGE18C10 	 2010 Total Population Age 18 (U.S. Census)
AGE19C10 	 2010 Total Population Age 19 (U.S. Census)
AGE20C10 	 2010 Total Population Age 20 (U.S. Census)
AGE21C10 	 2010 Total Population Age 21 (U.S. Census)
MAGE0C10 	 2010 Males Age <1 (U.S. Census)
MAGE1C10 	 2010 Males Age 1 (U.S. Census)
MAGE2C10 	 2010 Males Age 2 (U.S. Census)
MAGE3C10 	 2010 Males Age 3 (U.S. Census)
MAGE4C10 	 2010 Males Age 4 (U.S. Census)
MAGE5C10 	 2010 Males Age 5 (U.S. Census)
MAGE6C10 	 2010 Males Age 6 (U.S. Census)
MAGE7C10 	 2010 Males Age 7 (U.S. Census)
MAGE8C10 	 2010 Males Age 8 (U.S. Census)
MAGE9C10 	 2010 Males Age 9 (U.S. Census)
MAGE10C10 	 2010 Males Age 10 (U.S. Census)
MAGE11C10 	 2010 Males Age 11 (U.S. Census)
MAGE12C10 	 2010 Males Age 12 (U.S. Census)
MAGE13C10 	 2010 Males Age 13 (U.S. Census)
MAGE14C10 	 2010 Males Age 14 (U.S. Census)
MAGE15C10 	 2010 Males Age 15 (U.S. Census)
MAGE16C10 	 2010 Males Age 16 (U.S. Census)
MAGE17C10 	 2010 Males Age 17 (U.S. Census)
MAGE18C10 	 2010 Males Age 18 (U.S. Census)
MAGE19C10 	 2010 Males Age 19 (U.S. Census)
MAGE20C10 	 2010 Males Age 20 (U.S. Census)
MAGE21C10 	 2010 Males Age 21 (U.S. Census)
FAGE0C10 	 2010 Females Age <1 (U.S. Census)
FAGE1C10 	 2010 Females Age 1 (U.S. Census)
FAGE2C10 	 2010 Females Age 2 (U.S. Census)
FAGE3C10 	 2010 Females Age 3 (U.S. Census)
FAGE4C10 	 2010 Females Age 4 (U.S. Census)
FAGE5C10 	 2010 Females Age 5 (U.S. Census)
FAGE6C10 	 2010 Females Age 6 (U.S. Census)
FAGE7C10 	 2010 Females Age 7 (U.S. Census)
FAGE8C10 	 2010 Females Age 8 (U.S. Census)
FAGE9C10 	 2010 Females Age 9 (U.S. Census)
FAGE10C10 	 2010 Females Age 10 (U.S. Census)
FAGE11C10 	 2010 Females Age 11 (U.S. Census)
FAGE12C10 	 2010 Females Age 12 (U.S. Census)
FAGE13C10 	 2010 Females Age 13 (U.S. Census)
FAGE14C10 	 2010 Females Age 14 (U.S. Census)
FAGE15C10 	 2010 Females Age 15 (U.S. Census)
FAGE16C10 	 2010 Females Age 16 (U.S. Census)
FAGE17C10 	 2010 Females Age 17 (U.S. Census)
FAGE18C10 	 2010 Females Age 18 (U.S. Census)
FAGE19C10 	 2010 Females Age 19 (U.S. Census)
FAGE20C10 	 2010 Females Age 20 (U.S. Census)
FAGE21C10 	 2010 Females Age 21 (U.S. Census)
WHITE10 	 2010 White Population (U.S. Census)
BLACK10 	 2010 Black/African American Population (U.S. Census)
AMERIND10 	 2010 American Indian/Alaska Native Population (U.S. Census)
ASIAN10 	 2010 Asian Population (U.S. Census)
PACIFIC10 	 2010 Pacific Islander Population (U.S. Census)
OTHRACE10 	 2010 Other Race Population (U.S. Census)
H1RACE10 	 2010 Hispanic Population Reporting One Race (U.S. Census)
RACE2UP10 	 2010 Population of Two or More Races (U.S. Census)
HISPPOP10 	 2010 Hispanic Population (U.S. Census)
HWHITE10 	 2010 Hispanic White Population (U.S. Census)
HBLACK10 	 2010 Hispanic Black/African American Population (U.S. Census)
HAMERIND10 	 2010 Hispanic American Indian/Alaska Native Population (U.S. Census)
HASIAN10 	 2010 Hispanic Asian Population (U.S. Census)
HPACIFIC10 	 2010 Hispanic Pacific Islander Population (U.S. Census)
HOTHRACE10 	 2010 Hispanic Other Race Population (U.S. Census)
HRACE2UP10 	 2010 Hispanic Population Reporting Two or More Races (U.S. Census)
WHTNHISP10 	 2010 White Non-Hispanic Population (U.S. Census)
MINORITY10 	 2010 Minority Population (U.S. Census)
DIVINDX10 	 2010 Diversity Index (U.S. Census)
HPWHTHHR10 	 2010 Population Living in Households with a White Householder (U.S. Census)
HPBLKHHR10 	 2010 Population Living in Households with a Black/African American Householder (U.S. Census)
HPAIHHR10 	 2010 Population Living in Households with an American Indian/Alaska Native Householder (U.S. Census)
HPASNHHR10 	 2010 Population Living in Households with an Asian Householder (U.S. Census)
HPPIHHR10 	 2010 Population Living in Households with a Pacific Islander Householder (U.S. Census)
HPOTHHHR10 	 2010 Population Living in Households with an Other Race Householder (U.S. Census)
HPMLTHHR10 	 2010 Population Living in Households with a Multiple Races Householder (U.S. Census)
HPHSPHHR10 	 2010 Population Living in Households with a Hispanic Householder (U.S. Census)
HRACEBAS10 	 2010 Base for Hispanic Population by Race (U.S. Census)
RACEBASE10 	 2010 Base for Population by Race (U.S. Census)
POP1RACE10 	 2010 Population Reporting One Race (U.S. Census)
U18RBASE10 	 2010 Base for Population <18 by Race (U.S. Census)
ADRACBAS10 	 2010 Base for Population Age 18+ by Race (U.S. Census)
AD1RACE10 	 2010 Population Age 18+ Reporting One Race (U.S. Census)
HADRBASE10 	 2010 Base for Hispanic Population Age 18+ by Race (U.S. Census)
HAD1RACE10 	 2010 Hispanic Population Age 18+ Reporting One Race (U.S. Census)
MALHHRFH10 	 2010 Male Householder Living in Family Households (U.S. Census)
FEMHHRFH10 	 2010 Female Householder Living in Family Households (U.S. Census)
SPOUSEFH10 	 2010 Spouse Living in Family Households (U.S. Census)
BIOLCHFH10 	 2010 Biological Child Living in Family Households (U.S. Census)
ADPTCHFH10 	 2010 Adopted Child Living in Family Households (U.S. Census)
STEPCHFH10 	 2010 Stepchild Living in Family Households (U.S. Census)
GRNDCHFH10 	 2010 Grandchild Living in Family Households (U.S. Census)
BROSISFH10 	 2010 Brother or Sister Living in Family Households (U.S. Census)
PARENTFH10 	 2010 Parent Living in Family Households (U.S. Census)
PINLAWFH10 	 2010 Parent-in-law Living in Family Households (U.S. Census)
SDINLAWF10 	 2010 Son-in-law or Daughter-in-law Living in Family Households (U.S. Census)
OTHRELFH10 	 2010 Other Relative Living in Family Households (U.S. Census)
NONRELFH10 	 2010 Nonrelative Living in Family Households (U.S. Census)
MHHR1NF10 	 2010 Male Householder Living Alone in Nonfamily Households (U.S. Census)
MHHR2NF10 	 2010 Male Householder Not Living Alone in Nonfamily Households (U.S. Census)
FHHR1NF10 	 2010 Female Householder Living Alone in Nonfamily Households (U.S. Census)
FHHR2NF10 	 2010 Female Householder Not Living Alone in Nonfamily Households (U.S. Census)
NONRELNF10 	 2010 Nonrelative Living in Nonfamily Households (U.S. Census)
P65FAMHH10 	 2010 Population Age 65+ Living in Family Households (U.S. Census)
P65MHHRF10 	 2010 Male Householder Age 65+ Living in Family Households (U.S. Census)
P65FHHRF10 	 2010 Female Householder Age 65+ Living in Family Households (U.S. Census)
P65SPOUS10 	 2010 Spouse Age 65+ Living in Family Households (U.S. Census)
P65PARNT10 	 2010 Parent Age 65+ Living in Family Households (U.S. Census)
P65PINLW10 	 2010 Parent-in-law Age 65+ Living in Family Households (U.S. Census)
P65OREL10 	 2010 Other Relative Age 65+ Living in Family Households (U.S. Census)
P65NRELF10 	 2010 Nonrelative Age 65+ Living in Family Households (U.S. Census)
P65NFHH10 	 2010 Population Age 65+ Living in Nonfamily Households (U.S. Census)
P65MH1NF10 	 2010 Male Householder Age 65+ Living Alone in Nonfamily Households (U.S. Census)
P65MH2NF10 	 2010 Male Householder Age 65+ Not Living Alone in Nonfamily Households (U.S. Census)
P65FH1NF10 	 2010 Female Householder Age 65+ Living Alone in Nonfamily Households (U.S. Census)
P65FH2NF10 	 2010 Female Householder Age 65+ Not Living Alone in Nonfamily Households (U.S. Census)
P65NRLNF10 	 2010 Nonrelative Age 65+ Living in Nonfamily Households (U.S. Census)
GQPOP10 	 2010 Population in Group Quarters (U.S. Census)
GQINST10 	 2010 Institutionalized Population in Group Quarters (U.S. Census)
GQPRISON10 	 2010 Institutionalized Population in Adult Correctional Facilities (U.S. Census)
GQJUV10 	 2010 Institutionalized Population in Juvenile Facilities (U.S. Census)
GQNURS10 	 2010 Institutionalized Population in Nursing Facilities (U.S. Census)
GQOTINST10 	 2010 Institutionalized Population in Other Institutional (U.S. Census)
GQNINST10 	 2010 Noninstitutionalized Population in Group Quarters (U.S. Census)
GQCOLL10 	 2010 Noninstitutionalized Population in College Student Housing (U.S. Census)
GQMIL10 	 2010 Noninstitutionalized Population in Military Quarters (U.S. Census)
GQONINST10 	 2010 Noninstitutionalized Population in Other Noninstitutional (U.S. Census)
POPU18GQ10 	 2010 Population Age <18 in Group Quarters (U.S. Census)
U18INST10 	 2010 Institutionalized Population Age <18 in Group Quarters (U.S. Census)
U18NINST10 	 2010 Noninstitutionalized Population Age <18 in Group Quarters (U.S. Census)
POP65GQ10 	 2010 Population Age 65+ in Group Quarters (U.S. Census)
P65INST10 	 2010 Institutionalized Population Age 65+ in Group Quarters (U.S. Census)
P65NINST10 	 2010 Noninstitutionalized Population Age 65+ in Group Quarters:  (U.S. Census)
TOTHH10 	 2010 Total Households (U.S. Census)
HHU18C10 	 2010 Households with Population Age <18 (U.S. Census)
MLTGENHH10 	 2010 Multigenerational Households (Three or More Generations) (U.S. Census)
UNMARRMF10 	 2010 Unmarried-partner Households: Male and Female (U.S. Census)
UNMARRSS10 	 2010 Unmarried-partner Households: Same Sex (U.S. Census)
FAMSENR10 	 2010 Family Households with Population Age 65+ (U.S. Census)
HHGRW0010 	 2000-2010 Households Annual Compound Growth Rate (U.S. Census)
FAMGRW0010 	 2000-2010 Families Annual Compound Growth Rate (U.S. Census)
FAMHH10 	 2010 Total Family Households (U.S. Census)
HWFAMHH10 	 2010 Husband-wife Family Households (U.S. Census)
OTHFAMHH10 	 2010 Other Family Households (U.S. Census)
OFAMMHHR10 	 2010 Other Family Households with a Male Householder (U.S. Census)
OFAMFHHR10 	 2010 Other Family Households with a Female Householder (U.S. Census)
NONFAMHH10 	 2010 Total Nonfamily Households (U.S. Census)
NONFAM1P10 	 2010 Nonfamily Households with a Householder Living Alone (U.S. Census)
NF2PMHHR10 	 2010 Nonfamily 2+ Person Households with a Male Householder (U.S. Census)
NF2PFHHR10 	 2010 Nonfamily 2+ Person Households with a Female Householder (U.S. Census)
POPFAMHH10 	 2010 Population Living in Family Households (U.S. Census)
POPHWFAM10 	 2010 Population Living in Husband-wife Family Households (U.S. Census)
POPOFAMM10 	 2010 Population Living in Other Family Households with a Male Householder and No Spouse (U.S. Census)
POPOFAMF10 	 2010 Population Living in Other Family Households with a Female Householder and No Spouse (U.S. Census)
POPNFHH10 	 2010 Total Population Living in Nonfamily Households (U.S. Census)
POP1PNF10 	 2010 Population Living Alone in Nonfamily Households (U.S. Census)
POP2PNFM10 	 2010 Population Not Living Alone in Nonfamily Households with a Male Householder (U.S. Census)
POP2PNFF10 	 2010 Population Not Living Alone in Nonfamily Households with a Female Householder (U.S. Census)
AVGHHSZ10 	 2010 Average Household Size (U.S. Census)
AVGHSZOO10 	 2010 Average Household Size of Owner-occupied Housing Units (U.S. Census)
AVGHSZRO10 	 2010 Average Household Size of Renter-occupied Housing Units (U.S. Census)
AVGFMSZ10 	 2010 Average Family Size (U.S. Census)
AVGNFMSZ10 	 2010 Average Nonfamily Size (U.S. Census)
AVGHSZWH10 	 2010 Average Household Size of Households with a White Householder (U.S. Census)
AVGHSZBL10 	 2010 Average Household Size of Households with a Black/African American Householder (U.S. Census)
AVGHSZAI10 	 2010 Average Household Size of Households with an American Indian/Alaska Native Householder (U.S. Census)
AVGHSZAS10 	 2010 Average Household Size of Households with an Asian Householder (U.S. Census)
AVGHSZPI10 	 2010 Average Household Size of Households with a Pacific Islander Householder (U.S. Census)
AVGHSZOT10 	 2010 Average Household Size of Households with an Other Race Householder (U.S. Census)
AVGHSZ2R10 	 2010 Average Household Size of Households with a Multiple Races Householder (U.S. Census)
AVGHSZHS10 	 2010 Average Household Size of Households with a Hispanic Householder (U.S. Census)
FMSZBASE10 	 2010 Base for Family Households by Size (U.S. Census)
HHWHTHHR10 	 2010 Households with a White Householder (U.S. Census)
HHBLKHHR10 	 2010 Households with a Black/African American Householder (U.S. Census)
HHAIHHR10 	 2010 Households with an American Indian/Alaska Native Householder (U.S. Census)
HHASNHHR10 	 2010 Households with an Asian Householder (U.S. Census)
HHPIHHR10 	 2010 Households with a Pacific Islander Householder (U.S. Census)
HHOTHHHR10 	 2010 Households with an Other Race Householder (U.S. Census)
HHMLTHHR10 	 2010 Households with a Multiple Races Householder (U.S. Census)
HHHSPHHR10 	 2010 Households with a Hispanic Householder (U.S. Census)
FAMHHWHT10 	 2010 Family Households with a White Householder (U.S. Census)
FAMHHBLK10 	 2010 Family Households with a Black/African American Householder (U.S. Census)
FAMHHAI10 	 2010 Family Households with an American Indian/Alaska Native Householder (U.S. Census)
FAMHHASN10 	 2010 Family Households with an Asian Householder (U.S. Census)
FAMHHPI10 	 2010 Family Households with a Pacific Islander Householder (U.S. Census)
FAMHHOTH10 	 2010 Family Households with an Other Race Householder (U.S. Census)
FAMHHMLT10 	 2010 Family Households with a Multiple Races Householder (U.S. Census)
FAMHHWNH10 	 2010 Family Households with a White Non-Hispanic Householder (U.S. Census)
FAMHHHSP10 	 2010 Family Households with a Hispanic Householder (U.S. Census)
HWFAMWHT10 	 2010 Husband-wife Family Households with a White Householder (U.S. Census)
HWFAMBLK10 	 2010 Husband-wife Family Households with a Black/African American Householder (U.S. Census)
HWFAMAI10 	 2010 Husband-wife Family Households with an American Indian/Alaska Native Householder (U.S. Census)
HWFAMASN10 	 2010 Husband-wife Family Households with an Asian Householder (U.S. Census)
HWFAMPI10 	 2010 Husband-wife Family Households with a Pacific Islander Householder (U.S. Census)
HWFAMOTH10 	 2010 Husband-wife Family Households with an Other Race Householder (U.S. Census)
HWFAMMLT10 	 2010 Husband-wife Family Households with a Multiple Races Householder (U.S. Census)
HWFAMWNH10 	 2010 Husband-wife Family Households with a White Non-Hispanic Householder (U.S. Census)
HWFAMHSP10 	 2010 Husband-wife Family Households with a Hispanic Householder (U.S. Census)
OFAMWHT10 	 2010 Other Family Households with a White Householder (U.S. Census)
OFAMBLK10 	 2010 Other Family Households with a Black/African American Householder (U.S. Census)
OFAMAI10 	 2010 Other Family Households with an American Indian/Alaska Native Householder (U.S. Census)
OFAMASN10 	 2010 Other Family Households with an Asian Householder (U.S. Census)
OFAMPI10 	 2010 Other Family Households with a Pacific Islander Householder (U.S. Census)
OFAMOTH10 	 2010 Other Family Households with an Other Race Householder (U.S. Census)
OFAMMLT10 	 2010 Other Family Households with a Multiple Races Householder (U.S. Census)
OFAMWNH10 	 2010 Other Family Households with a White Non-Hispanic Householder (U.S. Census)
OFAMHSP10 	 2010 Other Family Households with a Hispanic Householder (U.S. Census)
NFAMWHT10 	 2010 Nonfamily Households with a White Householder (U.S. Census)
NFAMBLK10 	 2010 Nonfamily Households with a Black/African American Householder (U.S. Census)
NFAMAI10 	 2010 Nonfamily Households with an American Indian/Alaska Native Householder (U.S. Census)
NFAMASN10 	 2010 Nonfamily Households with an Asian Householder (U.S. Census)
NFAMPI10 	 2010 Nonfamily Households with a Pacific Islander Householder (U.S. Census)
NFAMOTH10 	 2010 Nonfamily Households with an Other Race Householder (U.S. Census)
NFAMMLT10 	 2010 Nonfamily Households with a Multiple Races Householder (U.S. Census)
NFAMWNH10 	 2010 Nonfamily Households with a White Non-Hispanic Householder (U.S. Census)
NFAMHSP10 	 2010 Nonfamily Households with a Hispanic Householder (U.S. Census)
HHRACBAS10 	 2010 Base for Households by Race of Householder (U.S. Census)
HWRACBAS10 	 2010 Base for Husband-wife Family Households by Race of Householder (U.S. Census)
FMRACBAS10 	 2010 Base for Family Households by Race of Householder (U.S. Census)
OFRACBAS10 	 2010 Base for Other Family Households by Race of Householder (U.S. Census)
NFRACBAS10 	 2010 Base for Nonfamily Households by Race of Householder (U.S. Census)
FAM2PERS10 	 2010 Family Households with 2 People (U.S. Census)
FAM3PERS10 	 2010 Family Households with 3 People (U.S. Census)
FAM4PERS10 	 2010 Family Households with 4 People (U.S. Census)
FAM5PERS10 	 2010 Family Households with 5 People (U.S. Census)
FAM6PERS10 	 2010 Family Households with 6 People (U.S. Census)
FAM7PERS10 	 2010 Family Households with 7+ People (U.S. Census)
NF1PERS10 	 2010 Nonfamily Households with 1 Person (U.S. Census)
NF2PERS10 	 2010 Nonfamily Households with 2 People (U.S. Census)
NF3PERS10 	 2010 Nonfamily Households with 3 People (U.S. Census)
NF4PERS10 	 2010 Nonfamily Households with 4 People (U.S. Census)
NF5PERS10 	 2010 Nonfamily Households with 5 People (U.S. Census)
NF6PERS10 	 2010 Nonfamily Households with 6 People (U.S. Census)
NF7PERS10 	 2010 Nonfamily Households with 7+ People (U.S. Census)
NFSZBASE10 	 2010 Base for Nonfamily Households by Size (U.S. Census)
FMHHR15C10 	 2010 Family Households with a Householder Age 15-24 (U.S. Census)
FMHHR25C10 	 2010 Family Households with a Householder Age 25-34 (U.S. Census)
FMHHR35C10 	 2010 Family Households with a Householder Age 35-44 (U.S. Census)
FMHHR45C10 	 2010 Family Households with a Householder Age 45-54 (U.S. Census)
FMHHR55C10 	 2010 Family Households with a Householder Age 55-64 (U.S. Census)
FMHHR65C10 	 2010 Family Households with a Householder Age 65-74 (U.S. Census)
FMHHR75C10 	 2010 Family Households with a Householder Age 75-84 (U.S. Census)
FMHHR85C10 	 2010 Family Households with a Householder Age 85+ (U.S. Census)
NFHHR15C10 	 2010 Nonfamily Households with a Householder Age 15-24 (U.S. Census)
NFHHR25C10 	 2010 Nonfamily Households with a Householder Age 25-34 (U.S. Census)
NFHHR35C10 	 2010 Nonfamily Households with a Householder Age 35-44 (U.S. Census)
NFHHR45C10 	 2010 Nonfamily Households with a Householder Age 45-54 (U.S. Census)
NFHHR55C10 	 2010 Nonfamily Households with a Householder Age 55-64 (U.S. Census)
NFHHR65C10 	 2010 Nonfamily Households with a Householder Age 65-74 (U.S. Census)
NFHHR75C10 	 2010 Nonfamily Households with a Householder Age 75-84 (U.S. Census)
NFHHR85C10 	 2010 Nonfamily Households with a Householder Age 85+ (U.S. Census)
FMAGEBAS10 	 2010 Base for Family Households by Age of Householder (U.S. Census)
NFAGEBAS10 	 2010 Base for Nonfamily Households by Age of Householder (U.S. Census)
HU18HWF10 	 2010 Husband-wife Family Households with Population Age <18 (U.S. Census)
HU18OFM10 	 2010 Other Family Households with a Male Householder and Population Age <18 (U.S. Census)
HU18OFF10 	 2010 Other Family Households with a Female Householder and Population Age <18 (U.S. Census)
HU18NFM10 	 2010 Nonfamily Households with a Male Householder and Population Age <18 (U.S. Census)
HU18NFF10 	 2010 Nonfamily Households with a Female Householder and Population Age <18 (U.S. Census)
HWFU18O10 	 2010 Husband-wife Families with Own Children Age <18 (U.S. Census)
HWFU6O10 	 2010 Husband-wife Families with Own Children Age <6 Only (U.S. Census)
HWF2AGEO10 	 2010 Husband-wife Families with Own Children Age <6 and 6-17 (U.S. Census)
HWF617O10 	 2010 Husband-wife Families with Own Children Age 6-17 Only (U.S. Census)
OFU18O10 	 2010 Other Family Households with Own Children Age <18 (U.S. Census)
OFU6O10 	 2010 Other Family Households with Own Children Age <6 Only (U.S. Census)
OF2AGEO10 	 2010 Other Family Households with Own Children Age <6 and 6-17 (U.S. Census)
OF617O10 	 2010 Other Family Households with Own Children Age 6-17 Only (U.S. Census)
OFMU18O10 	 2010 Other Family Households with a Male Householder and Own Children Age <18 (U.S. Census)
OFMU6O10 	 2010 Other Family Households with a Male Householder and Own Children Age <6 Only (U.S. Census)
OFM2AGEO10 	 2010 Other Family Households with a Male Householder and Own Children Age <6 and 6-17 (U.S. Census)
OFM617O10 	 2010 Other Family Households with a Male Householder and Own Children Age 6-17 Only (U.S. Census)
OFFU18O10 	 2010 Other Family Households with a Female Householder and Own Children Age <18 (U.S. Census)
OFFU6O10 	 2010 Other Family Households with a Female Householder and Own Children Age <6 Only (U.S. Census)
OFF2AGEO10 	 2010 Other Family Households with a Female Householder and Own Children Age <6 and 6-17 (U.S. Census)
OFF617O10 	 2010 Other Family Households with a Female Householder and Own Children Age 6-17 Only (U.S. Census)
FWHTU18O10 	 2010 Family Households with a White Householder and Own Children Age <18 (U.S. Census)
FBLKU18O10 	 2010 Family Households with a Black/African American Householder and Own Children Age <18 (U.S. Census)
FAIU18O10 	 2010 Family Households with an American Indian/Alaska Native Householder and Own Children Age <18 (U.S. Census)
FASNU18O10 	 2010 Family Households with an Asian Householder and Own Children Age <18 (U.S. Census)
FPIU18O10 	 2010 Family Households with a Pacific Islander Householder and Own Children Age <18 (U.S. Census)
FOTHU18O10 	 2010 Family Households with an Other Race Householder and Own Children Age <18 (U.S. Census)
FMLTU18O10 	 2010 Family Households with a Multiple Races Householder and Own Children Age <18 (U.S. Census)
FWNHU18O10 	 2010 Family Households with a White Non-Hispanic Householder and Own Children Age <18 (U.S. Census)
FHSPU18O10 	 2010 Family Households with a Hispanic Householder and Own Children Age <18 (U.S. Census)
HWFU18R10 	 2010 Husband-wife Families with Related Children Age <18 (U.S. Census)
HWFU6R10 	 2010 Husband-wife Families with Related Children Age <6 Only (U.S. Census)
HWF2AGER10 	 2010 Husband-wife Families with Related Children Age <6 and 6-17 (U.S. Census)
HWF617R10 	 2010 Husband-wife Families with Related Children Age 6-17 Only (U.S. Census)
OFU18R10 	 2010 Other Family Households with Related Children Age <18 (U.S. Census)
OFU6R10 	 2010 Other Family Households with Related Children Age <6 Only (U.S. Census)
OF2AGER10 	 2010 Other Family Households with Related Children Age <6 and 6-17 (U.S. Census)
OF617R10 	 2010 Other Family Households with Related Children Age 6-17 Only (U.S. Census)
OFMU18R10 	 2010 Other Family Households with a Male Householder and Related Children Age <18 (U.S. Census)
OFMU6R10 	 2010 Other Family Households with a Male Householder and Related Children Age <6 Only (U.S. Census)
OFM2AGER10 	 2010 Other Family Households with a Male Householder and Related Children Age <6 and 6-17 (U.S. Census)
OFM617R10 	 2010 Other Family Households with a Male Householder and Related Children Age 6-17 Only (U.S. Census)
OFFU18R10 	 2010 Other Family Households with a Female Householder and Related Children Age <18 (U.S. Census)
OFFU6R10 	 2010 Other Family Households with a Female Householder and Related Children Age <6 Only (U.S. Census)
OFF2AGER10 	 2010 Other Family Households with a Female Householder and Related Children Age <6 and 6-17 (U.S. Census)
OFF617R10 	 2010 Other Family Households with a Female Householder and Related Children Age 6-17 Only (U.S. Census)
FWHTU18R10 	 2010 Family Households with a White Householder and Related Children Age <18 (U.S. Census)
FBLKU18R10 	 2010 Family Households with a Black/African American Householder and Related Children Age <18 (U.S. Census)
FAIU18R10 	 2010 Family Households with an American Indian/Alaska Native Householder and Related Children Age <18 (U.S. Census)
FASNU18R10 	 2010 Family Households with an Asian Householder and Related Children Age <18 (U.S. Census)
FPIU18R10 	 2010 Family Households with a Pacific Islander Householder and Related Children Age <18 (U.S. Census)
FOTHU18R10 	 2010 Family Households with an Other Race Householder and Related Children Age <18 (U.S. Census)
FMLTU18R10 	 2010 Family Households with a Multiple Races Householder and Related Children Age <18 (U.S. Census)
FWNHU18R10 	 2010 Family Households with a White Non-Hispanic Householder and Related Children Age <18 (U.S. Census)
FHSPU18R10 	 2010 Family Households with a Hispanic Householder and Related Children Age <18 (U.S. Census)
HHNU18C10 	 2010 Households with No Population Age <18 (U.S. Census)
HNU18HWF10 	 2010 Husband-wife Family Households with No Population Age <18 (U.S. Census)
HNU18OFM10 	 2010 Other Family Households with a Male Householder and No Population Age <18 (U.S. Census)
HNU18OFF10 	 2010 Other Family Households with a Female Householder and No Population Age <18 (U.S. Census)
HNU18NFM10 	 2010 Nonfamily Households with a Male Householder and No Population Age <18 (U.S. Census)
HNU18NFF10 	 2010 Nonfamily Households with a Female Householder and No Population Age <18 (U.S. Census)
TOTHU10 	 2010 Total Housing Units (U.S. Census)
OWNER10 	 2010 Owner-occupied Housing Units (U.S. Census)
OOMORT10 	 2010 Owner-occupied Housing Units with a Mortgage/loan (U.S. Census)
OONOMORT10 	 2010 Owner-occupied Housing Units Owned Free and Clear (U.S. Census)
RENTER10 	 2010 Renter-occupied Housing Units (U.S. Census)
POPOWN10 	 2010 Population Living in Owner-occupied Housing Units (U.S. Census)
POPRENT10 	 2010 Population Living in Renter-occupied Housing Units (U.S. Census)
POPOWNM10 	 2010 Population Living in Occupied Housing units Owned with a Mortgage/Loan (U.S. Census)
POPOWNNM10 	 2010 Population Living in Occupied Housing Units Owned Free and Clear (U.S. Census)
URHUBASE10 	 2010 Urban/Rural Housing Unit Base (U.S. Census)
URBARHU10 	 2010 Housing Units Inside Urbanized Areas (U.S. Census)
URBCLHU10 	 2010 Housing Units Inside Urban Clusters (U.S. Census)
RURALHU10 	 2010 Rural Housing Units  (U.S. Census)
URBANHU10 	 2010 Urban Housing Units (U.S. Census)
OOWHTHHR10 	 2010 Owner-occupied Housing Units with a White Householder (U.S. Census)
OOBLKHHR10 	 2010 Owner-occupied Housing Units with a Black/African American Householder (U.S. Census)
OOAIHHR10 	 2010 Owner-occupied Housing Units with an American Indian/Alaska Native Householder (U.S. Census)
OOASNHHR10 	 2010 Owner-occupied Housing Units with an Asian Householder (U.S. Census)
OOPIHHR10 	 2010 Owner-occupied Housing Units with a Pacific Islander Householder (U.S. Census)
OOOTHHHR10 	 2010 Owner-occupied Housing Units with an Other Race Householder (U.S. Census)
OOMLTHHR10 	 2010 Owner-occupied Housing Units with a Multiple Races Householder (U.S. Census)
OOHSPHHR10 	 2010 Owner-occupied Housing Units with a Hispanic Householder (U.S. Census)
ROWHTHHR10 	 2010 Renter-occupied Housing Units with a White Householder (U.S. Census)
ROBLKHHR10 	 2010 Renter-occupied Housing Units with a Black/African American Householder (U.S. Census)
ROAIHHR10 	 2010 Renter-occupied Housing Units with an American Indian/Alaska Native Householder (U.S. Census)
ROASNHHR10 	 2010 Renter-occupied Housing Units with an Asian Householder (U.S. Census)
ROPIHHR10 	 2010 Renter-occupied Housing Units with a Pacific Islander Householder (U.S. Census)
ROOTHHHR10 	 2010 Renter-occupied Housing Units with an Other Race Householder (U.S. Census)
ROMLTHHR10 	 2010 Renter-occupied Housing Units with a Multiple Races Householder (U.S. Census)
ROHSPHHR10 	 2010 Renter-occupied Housing Units with a Hispanic Householder (U.S. Census)
OORACBAS10 	 2010 Base for Owner-occupied Housing Units by Race of Householder (U.S. Census)
RORACBAS10 	 2010 Base for Renter-occupied Housing Units by Race of Householder (U.S. Census)
OWN1PERS10 	 2010 Owner-occupied Housing Units with 1 Person (U.S. Census)
OWN2PERS10 	 2010 Owner-occupied Housing Units with 2 People (U.S. Census)
OWN3PERS10 	 2010 Owner-occupied Housing Units with 3 People (U.S. Census)
OWN4PERS10 	 2010 Owner-occupied Housing Units with 4 People (U.S. Census)
OWN5PERS10 	 2010 Owner-occupied Housing Units with 5 People (U.S. Census)
OWN6PERS10 	 2010 Owner-occupied Housing Units with 6 People (U.S. Census)
OWN7PERS10 	 2010 Owner-occupied Housing Units with 7+ People (U.S. Census)
OOSZBASE10 	 2010 Base for Owner-occupied Housing Units by Size (U.S. Census)
RNT1PERS10 	 2010 Renter-occupied Housing Units with 1 Person (U.S. Census)
RNT2PERS10 	 2010 Renter-occupied Housing Units with 2 People (U.S. Census)
RNT3PERS10 	 2010 Renter-occupied Housing Units with 3 People (U.S. Census)
RNT4PERS10 	 2010 Renter-occupied Housing Units with 4 People (U.S. Census)
RNT5PERS10 	 2010 Renter-occupied Housing Units with 5 People (U.S. Census)
RNT6PERS10 	 2010 Renter-occupied Housing Units with 6 People (U.S. Census)
RNT7PERS10 	 2010 Renter-occupied Housing Units with 7+ People (U.S. Census)
ROSZBASE10 	 2010 Base for Renter-occupied Housing Units by Size (U.S. Census)
OOHHR15C10 	 2010 Owner-occupied Housing Units with a Householder Age 15-24 (U.S. Census)
OOHHR25C10 	 2010 Owner-occupied Housing Units with a Householder Age 25-34 (U.S. Census)
OOHHR35C10 	 2010 Owner-occupied Housing Units with a Householder Age 35-44 (U.S. Census)
OOHHR45C10 	 2010 Owner-occupied Housing Units with a Householder Age 45-54 (U.S. Census)
OOHHR55C10 	 2010 Owner-occupied Housing Units with a Householder Age 55-64 (U.S. Census)
OOHHR65C10 	 2010 Owner-occupied Housing Units with a Householder Age 65-74 (U.S. Census)
OOHHR75C10 	 2010 Owner-occupied Housing Units with a Householder Age 75-84 (U.S. Census)
OOHHR85C10 	 2010 Owner-occupied Housing Units with a Householder Age 85+ (U.S. Census)
ROHHR15C10 	 2010 Renter-occupied Housing Units with a Householder Age 15-24 (U.S. Census)
ROHHR25C10 	 2010 Renter-occupied Housing Units with a Householder Age 25-34 (U.S. Census)
ROHHR35C10 	 2010 Renter-occupied Housing Units with a Householder Age 35-44 (U.S. Census)
ROHHR45C10 	 2010 Renter-occupied Housing Units with a Householder Age 45-54 (U.S. Census)
ROHHR55C10 	 2010 Renter-occupied Housing Units with a Householder Age 55-64 (U.S. Census)
ROHHR65C10 	 2010 Renter-occupied Housing Units with a Householder Age 65-74 (U.S. Census)
ROHHR75C10 	 2010 Renter-occupied Housing Units with a Householder Age 75-84 (U.S. Census)
ROHHR85C10 	 2010 Renter-occupied Housing Units with a Householder Age 85+ (U.S. Census)
VACANT10 	 2010 Vacant Housing Units (U.S. Census)
VACRENT10 	 2010 Vacant Housing Units: For Rent (U.S. Census)
VACRNTED10 	 2010 Vacant Housing Units: Rented - Not Occupied (U.S. Census)
VACSALE10 	 2010 Vacant Housing Units: For Sale Only (U.S. Census)
VACSOLD10 	 2010 Vacant Housing Units: Sold - Not Occupied (U.S. Census)
VACSEAS10 	 2010 Vacant Housing Units: Seasonal/Recreational/Occasional Use (U.S. Census)
VACMIGR10 	 2010 Vacant Housing Units: For Migrant Workers (U.S. Census)
VACOTHER10 	 2010 Vacant Housing Units: Other Vacant (U.S. Census)
VACBASE10 	 2010 Base for Vacant Housing Units by Vacancy Status (U.S. Census)
TOTPOP00 	 2000 Total Population (U.S. Census)
HHPOP00 	 2000 Population in Households (U.S. Census)
FAMPOP00 	 2000 Population in Families (U.S. Census)
TOTHH00 	 2000 Total Households (U.S. Census)
FAMHH00 	 2000 Total Family Households (U.S. Census)
TOTHU00 	 2000 Total Housing Units (U.S. Census)
OWNER00 	 2000 Owner Occupied Housing Units (U.S. Census)
RENTER00 	 2000 Renter Occupied Housing Units (U.S. Census)
TSEGNUM 	 2018 Dominant Tapestry Number (Esri)
TSEGCODE 	 2018 Dominant Tapestry Segment (Esri)
TSEGNAME 	 2018 Dominant Tapestry Segment Name (Esri)
THHBASE 	 2018 Base for Tapestry Segmentation Households (Esri)
THH01 	 2018 Top Tier (1A) Tapestry Households (Esri)
THH02 	 2018 Professional Pride (1B) Tapestry Households (Esri)
THH03 	 2018 Boomburbs (1C) Tapestry Households (Esri)
THH04 	 2018 Savvy Suburbanites (1D) Tapestry Households (Esri)
THH05 	 2018 Exurbanites (1E) Tapestry Households (Esri)
THH06 	 2018 Urban Chic (2A) Tapestry Households (Esri)
THH07 	 2018 Pleasantville (2B) Tapestry Households (Esri)
THH08 	 2018 Pacific Heights (2C) Tapestry Households (Esri)
THH09 	 2018 Enterprising Professionals (2D) Tapestry Households (Esri)
THH10 	 2018 Laptops and Lattes (3A) Tapestry Households (Esri)
THH11 	 2018 Metro Renters (3B) Tapestry Households (Esri)
THH12 	 2018 Trendsetters (3C) Tapestry Households (Esri)
THH13 	 2018 Soccer Moms (4A) Tapestry Households (Esri)
THH14 	 2018 Home Improvement (4B) Tapestry Households (Esri)
THH15 	 2018 Middleburg (4C) Tapestry Households (Esri)
THH16 	 2018 Comfortable Empty Nesters (5A) Tapestry Households (Esri)
THH17 	 2018 In Style (5B) Tapestry Households (Esri)
THH18 	 2018 Parks and Rec (5C) Tapestry Households (Esri)
THH19 	 2018 Rustbelt Traditions (5D) Tapestry Households (Esri)
THH20 	 2018 Midlife Constants (5E) Tapestry Households (Esri)
THH21 	 2018 Green Acres (6A) Tapestry Households (Esri)
THH22 	 2018 Salt of the Earth (6B) Tapestry Households (Esri)
THH23 	 2018 The Great Outdoors (6C) Tapestry Households (Esri)
THH24 	 2018 Prairie Living (6D) Tapestry Households (Esri)
THH25 	 2018 Rural Resort Dwellers (6E) Tapestry Households (Esri)
THH26 	 2018 Heartland Communities (6F) Tapestry Households (Esri)
THH27 	 2018 Up and Coming Families (7A) Tapestry Households (Esri)
THH28 	 2018 Urban Villages (7B) Tapestry Households (Esri)
THH29 	 2018 American Dreamers (7C) Tapestry Households (Esri)
THH30 	 2018 Barrios Urbanos (7D) Tapestry Households (Esri)
THH31 	 2018 Valley Growers (7E) Tapestry Households (Esri)
THH32 	 2018 Southwestern Families (7F) Tapestry Households (Esri)
THH33 	 2018 City Lights (8A) Tapestry Households (Esri)
THH34 	 2018 Emerald City (8B) Tapestry Households (Esri)
THH35 	 2018 Bright Young Professionals (8C) Tapestry Households (Esri)
THH36 	 2018 Downtown Melting Pot (8D) Tapestry Households (Esri)
THH37 	 2018 Front Porches (8E) Tapestry Households (Esri)
THH38 	 2018 Old and Newcomers (8F) Tapestry Households (Esri)
THH39 	 2018 Hardscrabble Road (8G) Tapestry Households (Esri)
THH40 	 2018 Silver & Gold (9A) Tapestry Households (Esri)
THH41 	 2018 Golden Years (9B) Tapestry Households (Esri)
THH42 	 2018 The Elders (9C) Tapestry Households (Esri)
THH43 	 2018 Senior Escapes (9D) Tapestry Households (Esri)
THH44 	 2018 Retirement Communities (9E) Tapestry Households (Esri)
THH45 	 2018 Social Security Set (9F) Tapestry Households (Esri)
THH46 	 2018 Southern Satellites (10A) Tapestry Households (Esri)
THH47 	 2018 Rooted Rural (10B) Tapestry Households (Esri)
THH48 	 2018 Diners & Miners (10C) Tapestry Households (Esri)
THH49 	 2018 Down the Road (10D) Tapestry Households (Esri)
THH50 	 2018 Rural Bypasses (10E) Tapestry Households (Esri)
THH51 	 2018 City Strivers (11A) Tapestry Households (Esri)
THH52 	 2018 Young and Restless (11B) Tapestry Households (Esri)
THH53 	 2018 Metro Fusion (11C) Tapestry Households (Esri)
THH54 	 2018 Set to Impress (11D) Tapestry Households (Esri)
THH55 	 2018 City Commons (11E) Tapestry Households (Esri)
THH56 	 2018 Family Foundations (12A) Tapestry Households (Esri)
THH57 	 2018 Traditional Living (12B) Tapestry Households (Esri)
THH58 	 2018 Small Town Simplicity (12C) Tapestry Households (Esri)
THH59 	 2018 Modest Income Homes (12D) Tapestry Households (Esri)
THH60 	 2018 International Marketplace (13A) Tapestry Households (Esri)
THH61 	 2018 Las Casas (13B) Tapestry Households (Esri)
THH62 	 2018 NeWest Residents (13C) Tapestry Households (Esri)
THH63 	 2018 Fresh Ambitions (13D) Tapestry Households (Esri)
THH64 	 2018 High Rise Renters (13E) Tapestry Households (Esri)
THH65 	 2018 Military Proximity (14A) Tapestry Households (Esri)
THH66 	 2018 College Towns (14B) Tapestry Households (Esri)
THH67 	 2018 Dorms to Diplomas (14C) Tapestry Households (Esri)
THH68 	 2018 Unclassified (15) Tapestry Households (Esri)
TLIFECODE 	 2018 Dominant Tapestry LifeMode Group Code
TLIFENAME 	 2018 Dominant Tapestry LifeMode Group Name
THHGRPL1 	 2018 Affluent Estates Tapestry LifeMode Group L1 Households (Esri)
THHGRPL2 	 2018 Upscale Avenues Tapestry LifeMode Group L2 Households (Esri)
THHGRPL3 	 2018 Uptown Individuals Tapestry LifeMode Group L3 Households (Esri)
THHGRPL4 	 2018 Family Landscapes Tapestry LifeMode Group L4 Households (Esri)
THHGRPL5 	 2018 GenXurban Tapestry LifeMode Group L5 Households (Esri)
THHGRPL6 	 2018 Cozy Country Living Tapestry LifeMode Group L6 Households (Esri)
THHGRPL7 	 2018 Ethnic Enclaves Tapestry LifeMode Group L7 Households (Esri)
THHGRPL8 	 2018 Middle Ground Tapestry LifeMode Group L8 Households (Esri)
THHGRPL9 	 2018 Senior Styles Tapestry LifeMode Group L9 Households (Esri)
THHGRPL10 	 2018 Rustic Outposts Tapestry LifeMode Group L10 Households (Esri)
THHGRPL11 	 2018 Midtown Singles Tapestry LifeMode Group L11 Households (Esri)
THHGRPL12 	 2018 Hometown Tapestry LifeMode Group L12 Households (Esri)
THHGRPL13 	 2018 Next Wave Tapestry LifeMode Group L13 Households (Esri)
THHGRPL14 	 2018 Scholars and Patriots Tapestry LifeMode Group L14 Households (Esri)
THHGRPU1 	 2018 Principal Urban Center Tapestry Urbanization Group U1 Households (Esri)
THHGRPU2 	 2018 Urban Periphery Tapestry Urbanization Group U2 Households (Esri)
THHGRPU3 	 2018 Metro Cities Tapestry Urbanization Group U3 Households (Esri)
THHGRPU4 	 2018 Suburban Periphery Tapestry Urbanization Group U4 Households (Esri)
THHGRPU5 	 2018 Semirural Tapestry Urbanization Group U5 Households (Esri)
THHGRPU6 	 2018 Rural Tapestry Urbanization Group U6 Households (Esri)
TADULTBASE 	 2018 Base for Tapestry Segmentation Adult Population (Esri)
TADULT01 	 2018 Top Tier (1A) Tapestry Adult Population (Esri)
TADULT02 	 2018 Professional Pride (1B) Tapestry Adult Population (Esri)
TADULT03 	 2018 Boomburbs (1C) Tapestry Adult Population (Esri)
TADULT04 	 2018 Savvy Suburbanites (1D) Tapestry Adult Population (Esri)
TADULT05 	 2018 Exurbanites (1E) Tapestry Adult Population (Esri)
TADULT06 	 2018 Urban Chic (2A) Tapestry Adult Population (Esri)
TADULT07 	 2018 Pleasantville (2B) Tapestry Adult Population (Esri)
TADULT08 	 2018 Pacific Heights (2C)Tapestry Adult Population (Esri)
TADULT09 	 2018 Enterprising Professionals (2D) Tapestry Adult Population (Esri)
TADULT10 	 2018 Laptops and Lattes (3A) Tapestry Adult Population (Esri)
TADULT11 	 2018 Metro Renters (3B) Tapestry Adult Population (Esri)
TADULT12 	 2018 Trendsetters (3C) Tapestry Adult Population (Esri)
TADULT13 	 2018 Soccer Moms (4A) Tapestry Adult Population (Esri)
TADULT14 	 2018 Home Improvement (4B) Tapestry Adult Population (Esri)
TADULT15 	 2018 Middleburg (4C) Tapestry Adult Population (Esri)
TADULT16 	 2018 Comfortable Empty Nesters (5A) Tapestry Adult Population (Esri)
TADULT17 	 2018 In Style (5B) Tapestry Adult Population (Esri)
TADULT18 	 2018 Parks and Rec (5C) Tapestry Adult Population (Esri)
TADULT19 	 2018 Rustbelt Traditions (5D) Tapestry Adult Population (Esri)
TADULT20 	 2018 Midlife Constants (5E) Tapestry Adult Population (Esri)
TADULT21 	 2018 Green Acres (6A) Tapestry Adult Population (Esri)
TADULT22 	 2018 Salt of the Earth (6B) Tapestry Adult Population (Esri)
TADULT23 	 2018 The Great Outdoors (6C) Tapestry Adult Population (Esri)
TADULT24 	 2018 Prairie Living (6D) Tapestry Adult Population (Esri)
TADULT25 	 2018 Rural Resort Dwellers (6E) Tapestry Adult Population (Esri)
TADULT26 	 2018 Heartland Communities (6F) Tapestry Adult Population (Esri)
TADULT27 	 2018 Up and Coming Families (7A) Tapestry Adult Population (Esri)
TADULT28 	 2018 Urban Villages (7B) Tapestry Adult Population (Esri)
TADULT29 	 2018 American Dreamers (7C) Tapestry Adult Population (Esri)
TADULT30 	 2018 Barrios Urbanos (7D) Tapestry Adult Population (Esri)
TADULT31 	 2018 Valley Growers (7E) Tapestry Adult Population (Esri)
TADULT32 	 2018 Southwestern Families (7F) Tapestry Adult Population (Esri)
TADULT33 	 2018 City Lights (8A) Tapestry Adult Population (Esri)
TADULT34 	 2018 Emerald City (8B) Tapestry Adult Population (Esri)
TADULT35 	 2018 Bright Young Professionals (8C) Tapestry Adult Population (Esri)
TADULT36 	 2018 Downtown Melting Pot (8D) Tapestry Adult Population (Esri)
TADULT37 	 2018 Front Porches (8E) Tapestry Adult Population (Esri)
TADULT38 	 2018 Old and Newcomers (8F) Tapestry Adult Population (Esri)
TADULT39 	 2018 Hardscrabble Road (8G) Tapestry Adult Population (Esri)
TADULT40 	 2018 Silver & Gold (9A) Tapestry Adult Population (Esri)
TADULT41 	 2018 Golden Years (9B) Tapestry Adult Population (Esri)
TADULT42 	 2018 The Elders (9C) Tapestry Adult Population (Esri)
TADULT43 	 2018 Senior Escapes (9D) Tapestry Adult Population (Esri)
TADULT44 	 2018 Retirement Communities (9E) Tapestry Adult Population (Esri)
TADULT45 	 2018 Social Security Set (9F) Tapestry Adult Population (Esri)
TADULT46 	 2018 Southern Satellites (10A) Tapestry Adult Population (Esri)
TADULT47 	 2018 Rooted Rural (10B) Tapestry Adult Population (Esri)
TADULT48 	 2018 Diners & Miners (10C) Tapestry Adult Population (Esri)
TADULT49 	 2018 Down the Road (10D) Tapestry Adult Population (Esri)
TADULT50 	 2018 Rural Bypasses (10E) Tapestry Adult Population (Esri)
TADULT51 	 2018 City Strivers (11A) Tapestry Adult Population (Esri)
TADULT52 	 2018 Young and Restless (11B) Tapestry Adult Population (Esri)
TADULT53 	 2018 Metro Fusion (11C) Tapestry Adult Population (Esri)
TADULT54 	 2018 Set to Impress (11D) Tapestry Adult Population (Esri)
TADULT55 	 2018 City Commons (11E) Tapestry Adult Population (Esri)
TADULT56 	 2018 Family Foundations (12A) Tapestry Adult Population (Esri)
TADULT57 	 2018 Traditional Living (12B) Tapestry Adult Population (Esri)
TADULT58 	 2018 Small Town Simplicity (12C) Tapestry Adult Population (Esri)
TADULT59 	 2018 Modest Income Homes (12D) Tapestry Adult Population (Esri)
TADULT60 	 2018 International Marketplace (13A) Tapestry Adult Population (Esri)
TADULT61 	 2018 Las Casas (13B) Tapestry Adult Population (Esri)
TADULT62 	 2018 NeWest Residents (13C) Tapestry Adult Population (Esri)
TADULT63 	 2018 Fresh Ambitions (13D) Tapestry Adult Population (Esri)
TADULT64 	 2018 High Rise Renters (13E) Tapestry Adult Population (Esri)
TADULT65 	 2018 Military Proximity (14A) Tapestry Adult Population (Esri)
TADULT66 	 2018 College Towns (14B) Tapestry Adult Population (Esri)
TADULT67 	 2018 Dorms to Diplomas (14C) Tapestry Adult Population (Esri)
TADULT68 	 2018 Unclassified (15) Tapestry Adult Population (Esri)
Shape_Length 	 Shape_Length
Shape_Area 	 Shape_Area

# Removing columns for 2023,2010,2000
state_df = state_df.loc[:,~state_df.columns.str.contains('FY|10|00')]
len(state_df.columns)

894

# Removing columns for individual age
state_df = state_df.loc[:,~state_df.columns.str.startswith(('AGE','MAGE','FAGE'))]
len(state_df.columns)

640

# Removing columns for Industry, Occupation
state_df = state_df.loc[:,~state_df.columns.str.startswith(('IND','OCC'))]
len(state_df.columns)

596

# Removing Individual Income Columns
state_df = state_df.loc[:,~state_df.columns.str.contains('A15|A25|A35|A45|A55|A65|A75')]
len(state_df.columns)

366

# Removing columns for Disposable Income and Net Worth
state_df = state_df.loc[:,~state_df.columns.str.startswith(('DI','NW'))]
len(state_df.columns)

348

# Removing columns for Tapestry Segmentation
state_df = state_df.loc[:,~state_df.columns.str.startswith(('TSE','THH','TADULT'))]
len(state_df.columns)

# Removing columns for Home Value
state_df = state_df.loc[:,~state_df.columns.str.startswith(('VAL'))]
len(state_df.columns)

state_df.columns

Index(['AAGEBASECY', 'AGGDI_CY', 'AGGHINC_CY', 'AGGINC_CY', 'AGGNW_CY',
       'AIFBASE_CY', 'AIMBASE_CY', 'AMERIND_CY', 'AREA', 'ASIAN_CY',
       ...
       'VAL250K_CY', 'VAL2M_CY', 'VAL50K_CY', 'VAL750K_CY', 'VALBASE_CY',
       'WAGEBASECY', 'WHITE_CY', 'WHTFBASECY', 'WHTMBASECY', 'WIDOWED_CY'],
      dtype='object', length=348)

print(obgyn_count_df.shape)
print(state_df.shape)

(56, 2)
(51, 348)

Merge dataframes¶

# Merge provider count and women_df at state level
newstate_obgyn_df = pd.merge(obgyn_count_df,state_df,left_on='RegionAbbr', right_on='ST_ABBREV',how='inner')

newstate_obgyn_df.shape

(51, 350)

Correlation¶

test_newstate_df = newstate_obgyn_df.copy()
test_newstate_df.head()

test_newstate_df.drop(['RegionAbbr','OBJECTID','SHAPE','STATE_NAME','ST_ABBREV','NAME','Shape_Area','Shape_Length','TLIFENAME','TSEGCODE','TSEGNAME','ID','TLIFECODE'], axis=1, inplace=True)
test_newstate_df.head()

# Check Datatypes of different columns
g = test_newstate_df.columns.to_series().groupby(test_newstate_df.dtypes).groups
g

{dtype('int64'): Index(['AAGEBASECY', 'AGGDI_CY', 'AGGHINC_CY', 'AGGINC_CY', 'AGGNW_CY',
        'AIFBASE_CY', 'AIMBASE_CY', 'AMERIND_CY', 'ASIAN_CY', 'ASNFBASECY',
        ...
        'VAL250K_CY', 'VAL2M_CY', 'VAL50K_CY', 'VAL750K_CY', 'VALBASE_CY',
        'WAGEBASECY', 'WHITE_CY', 'WHTFBASECY', 'WHTMBASECY', 'WIDOWED_CY'],
       dtype='object', length=326),
 dtype('float64'): Index(['AREA', 'AVGFMSZ_CY', 'AVGHHSZ_CY', 'LANDAREA', 'MEDAGE_CY',
        'MEDFAGE_CY', 'MEDHHR_CY', 'MEDMAGE_CY', 'POPDENS_CY', 'UNEMPRT_CY'],
       dtype='object'),
 dtype('O'): Index(['Provider_Count'], dtype='object')}

# Change Provider Count to Float
test_newstate_df['Provider_Count'] = test_newstate_df['Provider_Count'].astype(float)

test_newstate_df.dtypes

Provider_Count    float64
AAGEBASECY          int64
AGGDI_CY            int64
AGGHINC_CY          int64
AGGINC_CY           int64
AGGNW_CY            int64
AIFBASE_CY          int64
AIMBASE_CY          int64
AMERIND_CY          int64
AREA              float64
ASIAN_CY            int64
ASNFBASECY          int64
ASNMBASECY          int64
ASSCDEG_CY          int64
AVGDI_CY            int64
AVGFMSZ_CY        float64
AVGHHSZ_CY        float64
AVGHINC_CY          int64
AVGNW_CY            int64
AVGVAL_CY           int64
BABYBOOMCY          int64
BACHDEG_CY          int64
BAGEBASECY          int64
BLACK_CY            int64
BLKFBASECY          int64
BLKMBASECY          int64
CIVLBFR_CY          int64
EDUCBASECY          int64
EMP_CY              int64
FAMHH_CY            int64
                   ...   
THHGRPL7            int64
THHGRPL8            int64
THHGRPL9            int64
THHGRPU1            int64
THHGRPU2            int64
THHGRPU3            int64
THHGRPU4            int64
THHGRPU5            int64
THHGRPU6            int64
TOTHH_CY            int64
TOTHU_CY            int64
TOTPOP_CY           int64
TSEGNUM             int64
UNEMPRT_CY        float64
UNEMP_CY            int64
VACANT_CY           int64
VAL0_CY             int64
VAL150K_CY          int64
VAL1M_CY            int64
VAL1PT5MCY          int64
VAL250K_CY          int64
VAL2M_CY            int64
VAL50K_CY           int64
VAL750K_CY          int64
VALBASE_CY          int64
WAGEBASECY          int64
WHITE_CY            int64
WHTFBASECY          int64
WHTMBASECY          int64
WIDOWED_CY          int64
Length: 337, dtype: object

test_newstate_df.head()

import seaborn as sns
sns.set(rc={'figure.figsize':(18,8.27)})
# sns.heatmap(statetest3.corr().values, statetest.columns, statetest.columns, sort_f=False)
corr = test_newstate_df.corr()
# sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)
sns.heatmap(corr, vmin=-1.0, vmax=1.0)

<matplotlib.axes._subplots.AxesSubplot at 0x1f4ac6e78d0>

pd.plotting.scatter_matrix(test_newstate_df)
plt.show()

Run PCA¶

principal component analysis is a method of extracting important variables (in form of components) from a large set of variables available in a data set. It extracts low dimensional set of features from a high dimensional data set with a motive to capture as much information as possible. With fewer variables, visualization also becomes much more meaningful. Let’s say we have a data set of dimension 300 (n) × 50 (p). n represents the number of observations and p represents number of predictors. each resultant dimension is a linear combination of p features. PCA works on numeric variables only. Also the data needs to be normalized. PCA finds the best fit line by maximizing the sum of squared distances from projected points to origin.

test_newstate_df.shape

(51, 337)

test_newstate_df.head()

Get a list of columns and their location¶

[(test_newstate_df.columns.get_loc(c),c) for c in test_newstate_df]

[(0, 'Provider_Count'),
 (1, 'AAGEBASECY'),
 (2, 'AGGDI_CY'),
 (3, 'AGGHINC_CY'),
 (4, 'AGGINC_CY'),
 (5, 'AGGNW_CY'),
 (6, 'AIFBASE_CY'),
 (7, 'AIMBASE_CY'),
 (8, 'AMERIND_CY'),
 (9, 'AREA'),
 (10, 'ASIAN_CY'),
 (11, 'ASNFBASECY'),
 (12, 'ASNMBASECY'),
 (13, 'ASSCDEG_CY'),
 (14, 'AVGDI_CY'),
 (15, 'AVGFMSZ_CY'),
 (16, 'AVGHHSZ_CY'),
 (17, 'AVGHINC_CY'),
 (18, 'AVGNW_CY'),
 (19, 'AVGVAL_CY'),
 (20, 'BABYBOOMCY'),
 (21, 'BACHDEG_CY'),
 (22, 'BAGEBASECY'),
 (23, 'BLACK_CY'),
 (24, 'BLKFBASECY'),
 (25, 'BLKMBASECY'),
 (26, 'CIVLBFR_CY'),
 (27, 'EDUCBASECY'),
 (28, 'EMP_CY'),
 (29, 'FAMHH_CY'),
 (30, 'FAMPOP_CY'),
 (31, 'FEM0_CY'),
 (32, 'FEM15_CY'),
 (33, 'FEM18UP_CY'),
 (34, 'FEM20_CY'),
 (35, 'FEM21UP_CY'),
 (36, 'FEM25_CY'),
 (37, 'FEM30_CY'),
 (38, 'FEM35_CY'),
 (39, 'FEM40_CY'),
 (40, 'FEM45_CY'),
 (41, 'FEM50_CY'),
 (42, 'FEM55_CY'),
 (43, 'FEM5_CY'),
 (44, 'FEM60_CY'),
 (45, 'FEM65_CY'),
 (46, 'FEM70_CY'),
 (47, 'FEM75_CY'),
 (48, 'FEM80_CY'),
 (49, 'FEM85_CY'),
 (50, 'FEMALES_CY'),
 (51, 'GED_CY'),
 (52, 'GENALPHACY'),
 (53, 'GENBASE_CY'),
 (54, 'GENX_CY'),
 (55, 'GENZ_CY'),
 (56, 'GQPOP_CY'),
 (57, 'GRADDEG_CY'),
 (58, 'HAGEBASECY'),
 (59, 'HHPOP_CY'),
 (60, 'HINC0_CY'),
 (61, 'HINC150_CY'),
 (62, 'HINC15_CY'),
 (63, 'HINC25_CY'),
 (64, 'HINC35_CY'),
 (65, 'HINC50_CY'),
 (66, 'HINC75_CY'),
 (67, 'HINCBASECY'),
 (68, 'HISPAI_CY'),
 (69, 'HISPASN_CY'),
 (70, 'HISPBLK_CY'),
 (71, 'HISPMLT_CY'),
 (72, 'HISPOTH_CY'),
 (73, 'HISPPI_CY'),
 (74, 'HISPPOP_CY'),
 (75, 'HISPWHT_CY'),
 (76, 'HSGRAD_CY'),
 (77, 'HSPFBASECY'),
 (78, 'HSPMBASECY'),
 (79, 'IAGEBASECY'),
 (80, 'LANDAREA'),
 (81, 'MAL18UP_CY'),
 (82, 'MAL21UP_CY'),
 (83, 'MALE0_CY'),
 (84, 'MALE15_CY'),
 (85, 'MALE20_CY'),
 (86, 'MALE25_CY'),
 (87, 'MALE30_CY'),
 (88, 'MALE35_CY'),
 (89, 'MALE40_CY'),
 (90, 'MALE45_CY'),
 (91, 'MALE50_CY'),
 (92, 'MALE55_CY'),
 (93, 'MALE5_CY'),
 (94, 'MALE60_CY'),
 (95, 'MALE65_CY'),
 (96, 'MALE70_CY'),
 (97, 'MALE75_CY'),
 (98, 'MALE80_CY'),
 (99, 'MALE85_CY'),
 (100, 'MALES_CY'),
 (101, 'MARBASE_CY'),
 (102, 'MARRIED_CY'),
 (103, 'MEDAGE_CY'),
 (104, 'MEDDI_CY'),
 (105, 'MEDFAGE_CY'),
 (106, 'MEDHHR_CY'),
 (107, 'MEDHINC_CY'),
 (108, 'MEDMAGE_CY'),
 (109, 'MEDNW_CY'),
 (110, 'MEDVAL_CY'),
 (111, 'MILLENN_CY'),
 (112, 'MINORITYCY'),
 (113, 'MLTFBASECY'),
 (114, 'MLTMBASECY'),
 (115, 'NEVMARR_CY'),
 (116, 'NHSPAI_CY'),
 (117, 'NHSPASN_CY'),
 (118, 'NHSPBLK_CY'),
 (119, 'NHSPMLT_CY'),
 (120, 'NHSPOTH_CY'),
 (121, 'NHSPPI_CY'),
 (122, 'NHSPWHT_CY'),
 (123, 'NOHS_CY'),
 (124, 'NONHISP_CY'),
 (125, 'OAGEBASECY'),
 (126, 'OLDRGENSCY'),
 (127, 'OTHFBASECY'),
 (128, 'OTHMBASECY'),
 (129, 'OTHRACE_CY'),
 (130, 'OWNER_CY'),
 (131, 'PACIFIC_CY'),
 (132, 'PAGEBASECY'),
 (133, 'PCI_CY'),
 (134, 'PIFBASE_CY'),
 (135, 'PIMBASE_CY'),
 (136, 'POP0_CY'),
 (137, 'POP15_CY'),
 (138, 'POP18UP_CY'),
 (139, 'POP20_CY'),
 (140, 'POP21UP_CY'),
 (141, 'POP25_CY'),
 (142, 'POP30_CY'),
 (143, 'POP35_CY'),
 (144, 'POP40_CY'),
 (145, 'POP45_CY'),
 (146, 'POP50_CY'),
 (147, 'POP55_CY'),
 (148, 'POP5_CY'),
 (149, 'POP60_CY'),
 (150, 'POP65_CY'),
 (151, 'POP70_CY'),
 (152, 'POP75_CY'),
 (153, 'POP80_CY'),
 (154, 'POP85_CY'),
 (155, 'POPDENS_CY'),
 (156, 'RACE2UP_CY'),
 (157, 'RACEBASECY'),
 (158, 'RENTER_CY'),
 (159, 'SMCOLL_CY'),
 (160, 'SOMEHS_CY'),
 (161, 'TADULT01'),
 (162, 'TADULT02'),
 (163, 'TADULT03'),
 (164, 'TADULT04'),
 (165, 'TADULT05'),
 (166, 'TADULT06'),
 (167, 'TADULT07'),
 (168, 'TADULT08'),
 (169, 'TADULT09'),
 (170, 'TADULT11'),
 (171, 'TADULT12'),
 (172, 'TADULT13'),
 (173, 'TADULT14'),
 (174, 'TADULT15'),
 (175, 'TADULT16'),
 (176, 'TADULT17'),
 (177, 'TADULT18'),
 (178, 'TADULT19'),
 (179, 'TADULT20'),
 (180, 'TADULT21'),
 (181, 'TADULT22'),
 (182, 'TADULT23'),
 (183, 'TADULT24'),
 (184, 'TADULT25'),
 (185, 'TADULT26'),
 (186, 'TADULT27'),
 (187, 'TADULT28'),
 (188, 'TADULT29'),
 (189, 'TADULT30'),
 (190, 'TADULT31'),
 (191, 'TADULT32'),
 (192, 'TADULT33'),
 (193, 'TADULT34'),
 (194, 'TADULT35'),
 (195, 'TADULT36'),
 (196, 'TADULT37'),
 (197, 'TADULT38'),
 (198, 'TADULT39'),
 (199, 'TADULT40'),
 (200, 'TADULT41'),
 (201, 'TADULT42'),
 (202, 'TADULT43'),
 (203, 'TADULT44'),
 (204, 'TADULT45'),
 (205, 'TADULT46'),
 (206, 'TADULT47'),
 (207, 'TADULT48'),
 (208, 'TADULT49'),
 (209, 'TADULT50'),
 (210, 'TADULT51'),
 (211, 'TADULT52'),
 (212, 'TADULT53'),
 (213, 'TADULT54'),
 (214, 'TADULT55'),
 (215, 'TADULT56'),
 (216, 'TADULT57'),
 (217, 'TADULT58'),
 (218, 'TADULT59'),
 (219, 'TADULT60'),
 (220, 'TADULT61'),
 (221, 'TADULT62'),
 (222, 'TADULT63'),
 (223, 'TADULT64'),
 (224, 'TADULT65'),
 (225, 'TADULT66'),
 (226, 'TADULT67'),
 (227, 'TADULT68'),
 (228, 'TADULTBASE'),
 (229, 'THH01'),
 (230, 'THH02'),
 (231, 'THH03'),
 (232, 'THH04'),
 (233, 'THH05'),
 (234, 'THH06'),
 (235, 'THH07'),
 (236, 'THH08'),
 (237, 'THH09'),
 (238, 'THH11'),
 (239, 'THH12'),
 (240, 'THH13'),
 (241, 'THH14'),
 (242, 'THH15'),
 (243, 'THH16'),
 (244, 'THH17'),
 (245, 'THH18'),
 (246, 'THH19'),
 (247, 'THH20'),
 (248, 'THH21'),
 (249, 'THH22'),
 (250, 'THH23'),
 (251, 'THH24'),
 (252, 'THH25'),
 (253, 'THH26'),
 (254, 'THH27'),
 (255, 'THH28'),
 (256, 'THH29'),
 (257, 'THH30'),
 (258, 'THH31'),
 (259, 'THH32'),
 (260, 'THH33'),
 (261, 'THH34'),
 (262, 'THH35'),
 (263, 'THH36'),
 (264, 'THH37'),
 (265, 'THH38'),
 (266, 'THH39'),
 (267, 'THH40'),
 (268, 'THH41'),
 (269, 'THH42'),
 (270, 'THH43'),
 (271, 'THH44'),
 (272, 'THH45'),
 (273, 'THH46'),
 (274, 'THH47'),
 (275, 'THH48'),
 (276, 'THH49'),
 (277, 'THH50'),
 (278, 'THH51'),
 (279, 'THH52'),
 (280, 'THH53'),
 (281, 'THH54'),
 (282, 'THH55'),
 (283, 'THH56'),
 (284, 'THH57'),
 (285, 'THH58'),
 (286, 'THH59'),
 (287, 'THH60'),
 (288, 'THH61'),
 (289, 'THH62'),
 (290, 'THH63'),
 (291, 'THH64'),
 (292, 'THH65'),
 (293, 'THH66'),
 (294, 'THH67'),
 (295, 'THH68'),
 (296, 'THHBASE'),
 (297, 'THHGRPL1'),
 (298, 'THHGRPL11'),
 (299, 'THHGRPL12'),
 (300, 'THHGRPL13'),
 (301, 'THHGRPL14'),
 (302, 'THHGRPL2'),
 (303, 'THHGRPL3'),
 (304, 'THHGRPL4'),
 (305, 'THHGRPL5'),
 (306, 'THHGRPL6'),
 (307, 'THHGRPL7'),
 (308, 'THHGRPL8'),
 (309, 'THHGRPL9'),
 (310, 'THHGRPU1'),
 (311, 'THHGRPU2'),
 (312, 'THHGRPU3'),
 (313, 'THHGRPU4'),
 (314, 'THHGRPU5'),
 (315, 'THHGRPU6'),
 (316, 'TOTHH_CY'),
 (317, 'TOTHU_CY'),
 (318, 'TOTPOP_CY'),
 (319, 'TSEGNUM'),
 (320, 'UNEMPRT_CY'),
 (321, 'UNEMP_CY'),
 (322, 'VACANT_CY'),
 (323, 'VAL0_CY'),
 (324, 'VAL150K_CY'),
 (325, 'VAL1M_CY'),
 (326, 'VAL1PT5MCY'),
 (327, 'VAL250K_CY'),
 (328, 'VAL2M_CY'),
 (329, 'VAL50K_CY'),
 (330, 'VAL750K_CY'),
 (331, 'VALBASE_CY'),
 (332, 'WAGEBASECY'),
 (333, 'WHITE_CY'),
 (334, 'WHTFBASECY'),
 (335, 'WHTMBASECY'),
 (336, 'WIDOWED_CY')]

PCA Using Standard Scalar¶

# Standard Scalar
from sklearn.preprocessing import StandardScaler
sc_state = StandardScaler()
newstate_df_std = sc_state.fit_transform(test_newstate_df)

from sklearn.preprocessing import PowerTransformer
pt_state = PowerTransformer(method='yeo-johnson')
newstate_df_pt = pt_state.fit_transform(test_newstate_df)

# Import and fit PCA
from sklearn.decomposition import PCA
# pca_statetest = PCA().fit(newstate_df_std)
# pca_newstate = pca_statetest.transform(newstate_df_std)
pca_statetest = PCA(n_components=8)
pca_newstate = pca_statetest.fit_transform(newstate_df_std)

pca_statetest.components_

array([[ 0.067079  ,  0.05809787,  0.06796729, ...,  0.06698423,
         0.06715825,  0.0671426 ],
       [-0.00357032,  0.07835758,  0.00763204, ..., -0.02473751,
        -0.02266122, -0.01930669],
       [ 0.03209029,  0.00308058,  0.00831319, ...,  0.01021552,
         0.00718204,  0.0188534 ],
       ...,
       [ 0.02761109, -0.02721144,  0.00358023, ..., -0.00915768,
        -0.01188302,  0.01111088],
       [-0.00190508, -0.02394761, -0.00424891, ...,  0.01495248,
         0.01407101, -0.00246226],
       [-0.00147688,  0.0013432 , -0.01253322, ..., -0.0157983 ,
        -0.01461408, -0.0041321 ]])

pca_statetest.explained_variance_ratio_

array([0.63796413, 0.11235257, 0.05632679, 0.03036847, 0.02757483,
       0.02411164, 0.01938572, 0.01406097])

Scree Plot showing variation explained by each PC¶

# Show percentage of variance explained by different principal components
sns.set(rc={'figure.figsize':(7,7)})
labels = ['PC' + str(x) for x in range(1,9)]
plt.plot(np.cumsum(pca_statetest.explained_variance_ratio_))
plt.bar(x=range(0,8), height=pca_statetest.explained_variance_ratio_, tick_label = labels)
plt.xlabel('Number of components')
# plt.yticks(range(0,1,0.1))
# plt.ylim(bottom=0.90)
plt.ylabel('Cumulative explained variance');

The variance explained by PC1 is ~64% and the cumulative variance explained by first 5 components is ~86.5%.

Plot first 2 PCs¶

# Create df for PCs with State Labels
pca_df = pd.DataFrame(data=pca_newstate, index=newstate_obgyn_df.RegionAbbr, columns = ['PC1','PC2','PC3','PC4','PC5','PC6','PC7','PC8'])
pca_df

per_var = np.round(pca_statetest.explained_variance_ratio_*100, decimals=1)
plt.scatter(pca_df.PC1, pca_df.PC2)
plt.xlabel('PC1 - {0}%'.format(per_var[0]))
plt.ylabel('PC2 - {0}%'.format(per_var[1]))
for sample in pca_df.index:
    plt.annotate(sample, (pca_df.PC1.loc[sample],pca_df.PC2.loc[sample]))

This graph of first 2 components suggest that the cluster of states on the left (DC, UT, AK, CT) are correlated with each other. Similarly states in the bottom center (GA, NC, MI etc) are correlated with each other. The separation of 2 clusters along x-axis suggests that states towards the left are very different from states towards the right.

Check loadings of PCs¶

componentdf = pd.DataFrame(data=pca_statetest.components_, columns = test_newstate_df.columns)
componentdf.head()

# Top 10 loading scores of PC1
loading_scores = pd.Series(data=pca_statetest.components_[0], index = test_newstate_df.columns)
sorted_loading_scores = loading_scores.abs().sort_values(ascending=False)
top_10 = sorted_loading_scores[0:10].index.values
print(loading_scores[top_10])

MAL21UP_CY    0.068160
MAL18UP_CY    0.068157
MARBASE_CY    0.068141
POP21UP_CY    0.068141
TADULTBASE    0.068141
POP18UP_CY    0.068141
EDUCBASECY    0.068133
HHPOP_CY      0.068126
RACEBASECY    0.068125
GENBASE_CY    0.068125
dtype: float64

These loading score values are very similar which means a lot of the variables played a role in creating the principal component. The loading score details can be seen in dataframe of components above. The composition also tells us that our component is not depend on a few variables but many variables were used.

Correlation Plot of first 5 PC¶

sns.set(rc={'figure.figsize':(32,8.27)})
sns.heatmap(pca_statetest.components_[0:4], xticklabels=test_newstate_df.columns, annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0x1f4bbe41320>

the value 0.144 for Feature Provider Count is the score of this feature on the PC1. This value tells us 'how much' the feature influences the PC (in our case the PC1).

So the higher the value, the higher the influence on the principal component.

PCA Using Yeo Johnson¶

from sklearn.preprocessing import PowerTransformer
pt_state = PowerTransformer(method='yeo-johnson')
newstate_df_pt = pt_state.fit_transform(test_newstate_df)

C:\Users\mohi9282\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\sklearn\preprocessing\data.py:2863: RuntimeWarning: divide by zero encountered in log
  loglike = -n_samples / 2 * np.log(x_trans.var())

# Import and fit PCA
from sklearn.decomposition import PCA
# pca_statetest = PCA().fit(newstate_df_std)
# pca_newstate = pca_statetest.transform(newstate_df_std)
pca_statetest = PCA(n_components=8)
pca_newstate = pca_statetest.fit_transform(newstate_df_pt)

pca_statetest.components_

array([[ 8.56827471e-02,  2.46501764e-17,  1.70612740e-17, ...,
         8.56642910e-02,  8.58807499e-02,  8.59396494e-02],
       [ 2.04009326e-02, -4.51530494e-17,  4.92165744e-17, ...,
        -1.89815983e-02, -1.92861618e-02, -1.18518428e-02],
       [-3.69484764e-02,  1.68812862e-17, -8.88588834e-17, ...,
        -2.92403533e-02, -2.52796280e-02, -4.20536595e-02],
       ...,
       [ 9.81709512e-03, -1.48631479e-16,  6.07594257e-17, ...,
         6.00700145e-03,  4.28828440e-03,  9.54607887e-03],
       [ 1.18446865e-02,  1.38316898e-17,  9.84220915e-20, ...,
        -2.12025122e-02, -1.56032701e-02,  1.48317374e-02],
       [ 6.02476248e-04, -1.75361435e-16, -6.85856629e-17, ...,
        -1.57583965e-02, -1.39048380e-02,  2.27678656e-02]])

pca_statetest.explained_variance_ratio_

array([0.57343958, 0.12373149, 0.07294693, 0.04454296, 0.02618836,
       0.01954974, 0.01720015, 0.01370818])

sum(pca_statetest.explained_variance_ratio_[:5])

0.8408493275144913

Scree Plot showing variation explained by each PC¶

# Show percentage of variance explained by different principal components
sns.set(rc={'figure.figsize':(7,7)})
labels = ['PC' + str(x) for x in range(1,9)]
plt.plot(np.cumsum(pca_statetest.explained_variance_ratio_))
plt.bar(x=range(0,8), height=pca_statetest.explained_variance_ratio_, tick_label = labels)
plt.xlabel('Number of components')
# plt.yticks(range(0,1,0.1))
# plt.ylim(bottom=0.90)
plt.ylabel('Cumulative explained variance');

The variance explained by PC1 is ~57% and the cumulative variance explained by first 5 components is ~84%.

Plot first 2 PCs¶

# Create df for PCs with State Labels
pca_df = pd.DataFrame(data=pca_newstate, index=newstate_obgyn_df.RegionAbbr, columns = ['PC1','PC2','PC3','PC4','PC5','PC6','PC7','PC8'])
pca_df

plt.figure(figsize=(20,10))
per_var = np.round(pca_statetest.explained_variance_ratio_*100, decimals=1)
plt.scatter(pca_df.PC1, pca_df.PC2)
plt.xlabel('PC1 - {0}%'.format(per_var[0]))
plt.ylabel('PC2 - {0}%'.format(per_var[1]))
for sample in pca_df.index:
    plt.annotate(sample, (pca_df.PC1.loc[sample],pca_df.PC2.loc[sample]))

This graph of first 2 components suggest that the cluster of states on the left (DC, UT, AK, CT) are correlated with each other. Similarly states in the bottom center (GA, NC, MI etc) are correlated with each other. The separation of 2 clusters along x-axis suggests that states towards the left are very different from states towards the right.

Check loadings of PCs¶

componentdf = pd.DataFrame(data=pca_statetest.components_, columns = test_newstate_df.columns)
componentdf.head()

# Top 10 loading scores of PC1
loading_scores = pd.Series(data=pca_statetest.components_[0], index = test_newstate_df.columns)
sorted_loading_scores = loading_scores.abs().sort_values(ascending=False)
top_10 = sorted_loading_scores[0:10].index.values
print(loading_scores[top_10])

OWNER_CY      0.087039
VALBASE_CY    0.087039
HINC35_CY     0.086732
FEM75_CY      0.086474
MALE80_CY     0.086432
OLDRGENSCY    0.086322
ASSCDEG_CY    0.086244
NONHISP_CY    0.086238
POP80_CY      0.086152
WIDOWED_CY    0.085940
dtype: float64

These loading score values are very similar which means a lot of the variables played a role in creating the principal component. The loading score details can be seen in dataframe of components above. The composition also tells us that our component is not depend on a few variables but many variables were used.

Correlation Plot of first 5 PC¶

sns.set(rc={'figure.figsize':(32,8.27)})
sns.heatmap(pca_statetest.components_[0:4], xticklabels=test_newstate_df.columns, annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0x2025bc56400>

Clustering¶

The idea of clustering is to find out if observations in the data naturally group together in some predictable way. In our case, we want to know if there is natural grouping that distinguishes some states from others. Standardization - most cluster analysis algorithms depend on the concept of measuring the distance between the different observations we're trying to cluster. If one of the variables is measured on a much larger scale than the other variables, then whatever measure we use will be overly influenced by the variable that has larger scale. So, we standardize our data to bring all variables to the same scale.

k-means¶

test_newstate_df.head()
# test_newstate_df[(test_newstate_df[[0, 1]] < 0).all(1)]
# test_newstate_df[(test_newstate_df.iloc[:,:] < 0).all(1)]

Determine optimal clusters using within cluster SSE¶

# Check if any values are 0 in the dataframe
# test_newstate_df.isnull().values.any()
test_newstate_df.columns[test_newstate_df.eq(0).any()].tolist()

['TADULT01',
 'TADULT02',
 'TADULT03',
 'TADULT04',
 'TADULT05',
 'TADULT06',
 'TADULT07',
 'TADULT08',
 'TADULT09',
 'TADULT11',
 'TADULT12',
 'TADULT13',
 'TADULT14',
 'TADULT15',
 'TADULT16',
 'TADULT17',
 'TADULT18',
 'TADULT19',
 'TADULT20',
 'TADULT21',
 'TADULT22',
 'TADULT23',
 'TADULT24',
 'TADULT25',
 'TADULT26',
 'TADULT27',
 'TADULT28',
 'TADULT29',
 'TADULT30',
 'TADULT31',
 'TADULT32',
 'TADULT33',
 'TADULT34',
 'TADULT35',
 'TADULT36',
 'TADULT37',
 'TADULT38',
 'TADULT39',
 'TADULT40',
 'TADULT42',
 'TADULT43',
 'TADULT44',
 'TADULT46',
 'TADULT47',
 'TADULT48',
 'TADULT49',
 'TADULT50',
 'TADULT51',
 'TADULT52',
 'TADULT53',
 'TADULT55',
 'TADULT56',
 'TADULT57',
 'TADULT58',
 'TADULT59',
 'TADULT60',
 'TADULT61',
 'TADULT62',
 'TADULT63',
 'TADULT64',
 'TADULT65',
 'TADULT67',
 'TADULT68',
 'THH01',
 'THH02',
 'THH03',
 'THH04',
 'THH05',
 'THH06',
 'THH07',
 'THH08',
 'THH09',
 'THH11',
 'THH12',
 'THH13',
 'THH14',
 'THH15',
 'THH16',
 'THH17',
 'THH18',
 'THH19',
 'THH20',
 'THH21',
 'THH22',
 'THH23',
 'THH24',
 'THH25',
 'THH26',
 'THH27',
 'THH28',
 'THH29',
 'THH30',
 'THH31',
 'THH32',
 'THH33',
 'THH34',
 'THH35',
 'THH36',
 'THH37',
 'THH38',
 'THH39',
 'THH40',
 'THH42',
 'THH43',
 'THH44',
 'THH46',
 'THH47',
 'THH48',
 'THH49',
 'THH50',
 'THH51',
 'THH52',
 'THH53',
 'THH55',
 'THH56',
 'THH57',
 'THH58',
 'THH59',
 'THH60',
 'THH61',
 'THH62',
 'THH63',
 'THH64',
 'THH65',
 'THH67',
 'THH68',
 'THHGRPL13',
 'THHGRPL2',
 'THHGRPL3',
 'THHGRPL4',
 'THHGRPL5',
 'THHGRPL6',
 'THHGRPL7',
 'THHGRPU1',
 'THHGRPU5',
 'THHGRPU6']

test_newstate_df['MEDHINC_CY'] = test_newstate_df['MEDHINC_CY'].astype(float)
type(test_newstate_df['MEDHINC_CY'][0])

numpy.float64

# Transform Data
from sklearn.preprocessing import PowerTransformer
pt_state = PowerTransformer(method='yeo-johnson')
newstate_df_pt = pt_state.fit_transform(test_newstate_df)

C:\Users\mohi9282\AppData\Local\Continuum\anaconda3\envs\arcgis\lib\site-packages\sklearn\preprocessing\data.py:2863: RuntimeWarning: divide by zero encountered in log
  loglike = -n_samples / 2 * np.log(x_trans.var())

from sklearn.cluster import KMeans
sns.set(rc={'figure.figsize':(15,8)})
distortions = []
for i in range(1,20):
    km = KMeans(n_clusters=i,
                init='k-means++',
                n_init=10,
                max_iter=300,
                random_state=0)
    km.fit(newstate_df_pt)
    distortions.append(km.inertia_)
    
plt.plot(range(1,20), distortions, marker='o')
plt.xlabel('No. of Clusters')
plt.ylabel('Within Cluster Sum of Squares')
plt.show()

Run KMeans with 4 clusters¶

sns.set(rc={'figure.figsize':(15,8)})
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=4)
# Fit the data
kmeans.fit(newstate_df_pt)
# Get the cluster labels
y_pred = kmeans.predict(newstate_df_pt)
# plot the cluster assignments and cluster centers
# newstate_df_std[:,0] includes all values for Provider Count and so on... #103 is MEDAGE_CY
plt.scatter(newstate_df_pt[:, 0], newstate_df_pt[:,103], c=y_pred, cmap="plasma")
plt.scatter(kmeans.cluster_centers_[:, 0],   
            kmeans.cluster_centers_[:, 103],
            marker='^', 
            c=[0,1,2,3], 
            s=100, 
            linewidth=2,
            cmap="plasma")
plt.xlabel("Feature 0")
plt.ylabel("Feature 1")

Text(0, 0.5, 'Feature 1')

kmeans.cluster_centers_

array([[ 0.32717284,  0.        ,  0.        , ...,  0.25436349,
         0.26167897,  0.14226632],
       [-1.24494478,  0.        ,  0.        , ..., -1.25310715,
        -1.25743991, -1.25824838],
       [ 1.3453939 ,  0.        ,  0.        , ...,  1.31024149,
         1.30931671,  1.33246836],
       [-0.06460048,  0.        ,  0.        , ...,  0.0221897 ,
         0.02154708,  0.09289306]])

kmeans.labels_

array([2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 0, 0, 3, 0, 0, 3, 3, 0, 3, 0,
       3, 0, 3, 3, 3, 0, 3, 3, 0, 0, 3, 3, 3, 3, 1, 3, 1, 1, 1, 1, 1, 1,
       1, 1, 1, 1, 1, 1, 1])

Identify which states belong to what cluster¶

# Create dataframe of Cluster labels and State Names
clusterdf = pd.DataFrame(kmeans.labels_, newstate_obgyn_df['RegionAbbr'])
clusterdf.reset_index(inplace=True)
clusterdf.columns = ['Region','Cluster']
clusterdf.head()

# Count of states in each cluster
clusterdf['Cluster'].value_counts()

3    15
1    14
2    11
0    11
Name: Cluster, dtype: int64

# States in each cluster
clusterdf.groupby('Cluster')['Region'].apply(list)

Cluster
0         [NJ, MA, MD, WA, AZ, CO, CT, MN, OR, NV, UT]
1    [HI, WV, DC, RI, NH, ID, DE, ME, MT, AK, VT, S...
2         [CA, TX, NY, FL, PA, IL, MI, OH, GA, NC, VA]
3    [MO, IN, TN, SC, LA, WI, KY, AL, OK, MS, KS, I...
Name: Region, dtype: object

sns.set(rc={'figure.figsize':(32,8.27)})
sns.heatmap(kmeans.cluster_centers_[0:4], xticklabels=test_newstate_df.columns, annot=True)

<matplotlib.axes._subplots.AxesSubplot at 0x1f4c219fd68>

	NAME	OBJECTID	SHAPE	ST_ABBREV	TOTPOP_CY
0	Adams County	1402	{"rings": [[[-10195353.0856, 3667011.907600000...	MS	31455
1	Alcorn County	1403	{"rings": [[[-9838473.3692, 4163284.5766000003...	MS	37398
2	Amite County	1404	{"rings": [[[-10139507.6094, 3675190.789200000...	MS	12573
3	Attala County	1405	{"rings": [[[-9957952.5787, 3933319.5100999996...	MS	19195
4	Benton County	1406	{"rings": [[[-9917285.3423, 4109418.0692000017...	MS	8621

	NAME	OBJECTID	SHAPE	ST_ABBREV	TOTPOP_CY	County	Provider_Count
0	Adams County	1402	{'rings': [[[-10195353.0856, 3667011.907600000...	MS	31455	Adams County	7
1	Alcorn County	1403	{'rings': [[[-9838473.3692, 4163284.5766000003...	MS	37398	Alcorn County	17
2	Amite County	1404	{'rings': [[[-10139507.6094, 3675190.789200000...	MS	12573	Amite County	1
3	Attala County	1405	{'rings': [[[-9957952.5787, 3933319.5100999996...	MS	19195	Attala County	1
4	Benton County	1406	{'rings': [[[-9917285.3423, 4109418.0692000017...	MS	8621	Benton County	1

	NAME	TOTPOP_CY	Provider_Count	people_per_prov
46	Marshall County	37137	1	37137
57	Pontotoc County	32007	1	32007
49	Neshoba County	29483	1	29483
61	Scott County	29144	1	29144
72	Union County	28734	1	28734
15	Covington County	20192	1	20192
25	Holmes County	19589	1	19589
3	Attala County	19195	1	19195
65	Stone County	18248	1	18248
8	Chickasaw County	17554	1	17554
64	Smith County	17382	1	17382
30	Jasper County	17235	1	17235
11	Clarke County	16309	1	16309
14	Copiah County	29217	2	14608.5
68	Tate County	29215	2	14607.5
66	Sunflower County	28285	2	14142.5
81	Yazoo County	27697	2	13848.5
58	Prentiss County	25416	2	12708
2	Amite County	12573	1	12573
55	Perry County	12423	1	12423
22	Hancock County	49209	4	12302.2
19	George County	23952	2	11976
50	Newton County	21801	2	10900.5
52	Oktibbeha County	50893	5	10178.6
47	Monroe County	36744	4	9186
4	Benton County	8621	1	8621
33	Jones County	68332	8	8541.5
54	Pearl River County	56971	7	8138.71
38	Lawrence County	13464	2	6732
45	Marion County	25925	4	6481.25
...	...	...	...	...
9	Choctaw County	8385	3	2795
10	Claiborne County	9861	4	2465.25
44	Madison County	110172	48	2295.25
1	Alcorn County	37398	17	2199.88
40	Lee County	86039	45	1911.98
60	Rankin County	152523	81	1883
37	Lauderdale County	78920	52	1517.69
17	Forrest County	75838	51	1487.02
23	Harrison County	206287	143	1442.57
35	Lafayette County	56405	40	1410.12
24	Hinds County	241686	274	882.066
36	Lamar County	59391	76	781.461
6	Calhoun County	14741	NaN	NaN
7	Carroll County	10761	NaN	NaN
20	Greene County	13703	NaN	NaN
27	Issaquena County	1403	NaN	NaN
28	Itawamba County	23998	NaN	NaN
34	Kemper County	11049	NaN	NaN
39	Leake County	22284	NaN	NaN
48	Montgomery County	10326	NaN	NaN
51	Noxubee County	11253	NaN	NaN
62	Sharkey County	4733	NaN	NaN
67	Tallahatchie County	15196	NaN	NaN
70	Tishomingo County	19971	NaN	NaN
71	Tunica County	10696	NaN	NaN
73	Walthall County	14910	NaN	NaN
76	Wayne County	20937	NaN	NaN
77	Webster County	10001	NaN	NaN
78	Wilkinson County	9745	NaN	NaN
80	Yalobusha County	12813	NaN	NaN

	ALAND	AWATER	B13016_001E	B13016_001M	B13016_002E	B13016_002M	B13016_003E	B13016_003M	B13016_004E	B13016_004M	B13016_005E	B13016_005M	B13016_006E	B13016_006M	B13016_007E	B13016_007M	B13016_008E	B13016_008M	B13016_009E	B13016_009M	B13016_010E	B13016_010M	B13016_011E	B13016_011M	B13016_012E	B13016_012M	B13016_013E	B13016_013M	B13016_014E	B13016_014M	B13016_015E	B13016_015M	B13016_016E	B13016_016M	B13016_017E	B13016_017M	B13016_calc_num15to19E	B13016_calc_num15to19M	B13016_calc_num20to24E	B13016_calc_num20to24M	B13016_calc_num25to29E	B13016_calc_num25to29M	B13016_calc_num30to34E	B13016_calc_num30to34M	B13016_calc_num35to39E	B13016_calc_num35to39M	B13016_calc_num40to44E	B13016_calc_num40to44M	B13016_calc_num45to50E	B13016_calc_num45to50M	B13016_calc_pct15to19E	B13016_calc_pct15to19M	B13016_calc_pct20to24E	B13016_calc_pct20to24M	B13016_calc_pct25to29E	B13016_calc_pct25to29M	B13016_calc_pct30to34E	B13016_calc_pct30to34M	B13016_calc_pct35to39E	B13016_calc_pct35to39M	B13016_calc_pct40to44E	B13016_calc_pct40to44M	B13016_calc_pct45to50E	B13016_calc_pct45to50M	B13016_calc_pctBirthsE	B13016_calc_pctBirthsM	GEOID	NAME	OBJECTID	SHAPE	STATENS	STUSPS	Shape__Area	Shape__Length
0	131174431216	4592944701	1150523	1402	58732	1690	2903	415	14484	792	18605	889	13911	884	6318	680	1790	423	721	193	1091791	1867	157636	1096	152944	1175	144825	1120	141620	1125	147991	2099	152552	2185	194223	1366	160539	1172	167428	1417	163430	1430	155531	1431	154309	2206	154342	2226	194944	1380	1.8	0.258167	8.7	0.467339	11.4	0.534766	8.9	0.562386	4.1	0.436770	1.2	0.273556	0.4	0.098968	5.1	0.146698	01	Alabama	1	{"rings": [[[-9805779.6231, 3536997.5778], [-9...	01779775	AL	1.901226e+11	2.312416e+06
1	1478588231566	277723861311	174163	485	10582	662	408	125	2240	276	3190	359	2763	326	1483	270	430	99	68	40	163581	789	22202	326	22666	352	25803	411	23760	384	21123	710	21134	779	26893	421	22610	349	24906	447	28993	546	26523	504	22606	760	21564	785	26961	423	1.8	0.552151	9.0	1.096348	11.0	1.220770	10.4	1.213076	6.6	1.173833	2.0	0.453323	0.3	0.148310	6.1	0.379401	02	Alaska	2	{"rings": [[[-19937057.4487, 6661392.0514], [-...	01785533	AK	8.208934e+12	6.399688e+07
2	294198661567	1027245114	1571003	1527	85196	2315	3290	346	18209	982	24282	1113	22146	1110	12496	754	3273	324	1500	303	1485807	2427	222499	969	213748	1310	201156	1099	196251	1121	196215	2313	204729	2415	251209	1483	225789	1029	231957	1637	225438	1564	218397	1578	208711	2433	208002	2437	252709	1514	1.5	0.153096	7.9	0.419714	10.8	0.488018	10.1	0.502940	6.0	0.354459	1.6	0.154673	0.6	0.119848	5.4	0.147141	04	Arizona	3	{"rings": [[[-12138854.2127, 4438965.0002], [-...	01779777	AZ	4.340581e+11	2.906214e+06
3	134768100673	2963631791	689530	1063	40901	1606	2514	379	11224	869	12726	754	8852	676	4012	546	1088	240	485	148	648629	1983	95325	724	89233	1111	86020	996	86174	898	88668	1665	91183	1443	112026	960	97839	817	100457	1410	98746	1249	95026	1124	92680	1752	92271	1463	112511	971	2.6	0.386776	11.2	0.850713	12.9	0.745972	9.3	0.702799	4.3	0.583413	1.2	0.259431	0.4	0.131490	5.9	0.232502	05	Arkansas	4	{"rings": [[[-9989043.0946, 4300705.9503], [-9...	00068085	AR	2.053261e+11	2.656502e+06
4	403483182192	20484637928	9642845	3558	478458	5207	16079	831	76861	2027	120625	2303	138924	3037	87689	2301	28439	1168	9841	670	9164387	6546	1258668	1367	1302552	2268	1324982	2293	1245434	3141	1207288	5298	1256240	5262	1569223	3493	1274747	1600	1379413	3042	1445607	3250	1384358	4369	1294977	5776	1284679	5390	1579064	3557	1.3	0.065170	5.6	0.146432	8.3	0.158202	10.0	0.217082	6.8	0.175101	2.2	0.090442	0.6	0.042407	5.0	0.053932	06	California	5	{"rings": [[[-13198544.1255, 3897778.6019], [-...	01779778	CA	6.500881e+11	5.274852e+06

	SHAPE	city	objectid	postal	region	regionabbr	subregion	user_entity_type	user_full_address	user_npi	user_provider_gender	user_taxonomy_code_1
0	{"x": -97.49116252599998, "y": 35.261905469000...	Norman	8	73072	Oklahoma	OK	Cleveland County	Individual	3400 W TECUMSEH RD, SUITE 205, NORMAN, OK 7307...	1.487658e+09	F	207V00000X
1	{"x": -120.00990533799995, "y": 46.31641132900...	Sunnyside	24	98944	Washington	WA	Yakima County	Individual	803 E LINCOLN AVE, , SUNNYSIDE, WA 989442383	1.265435e+09	F	207V00000X
2	{"x": -81.63739261199999, "y": 38.359802984000...	Charleston	26	25302	West Virginia	WV	Kanawha County	Individual	830 PENNSYLVANIA AVE, SUITE 108, CHARLESTON, W...	1.447253e+09	M	207V00000X
3	{"x": -81.63739261199999, "y": 38.359802984000...	Charleston	99	25302	West Virginia	WV	Kanawha County	Individual	830 PENNSYLVANIA AVE, STE 402, CHARLESTON, WV ...	1.487658e+09	M	207V00000X
4	{"x": -81.63739261199999, "y": 38.359802984000...	Charleston	172	25302	West Virginia	WV	Kanawha County	Individual	830 PENNSYLVANIA AVE, STE 402, CHARLESTON, WV ...	1.255334e+09	M	207V00000X

	OBJECTID	SHAPE	STATE_NAME	ST_ABBREV	TOTPOP_CY
0	1	{"rings": [[[-9747504.6398, 3539549.5786999986...	Alabama	AL	4968383
1	2	{"rings": [[[-19677908.5389, 6763775.151000001...	Alaska	AK	750876
2	3	{"rings": [[[-12138852.7978, 4438964.613399997...	Arizona	AZ	7132147
3	4	{"rings": [[[-9989041.8861, 4300705.307499997]...	Arkansas	AR	3067536
4	5	{"rings": [[[-13038833.5744, 3845968.519900001...	California	CA	39806791

	City	OBJECTID	Postal	Region	RegionAbbr	SHAPE	Subregion	user_entity_type	user_full_address	user_npi	user_provider_gender	user_taxonomy_code_1
0	Kailua	50	96734	Hawaii	HI	{"x": -157.75842118599996, "y": 21.38069594300...	City and County of Honolulu	Individual	642 ULUKAHIKI ST, SUITE 300, KAILUA, HI 967344400	1366445306	M	2084N0400X
1	Rialto	110	92376	California	CA	{"x": -117.35748267199995, "y": 34.09373249300...	San Bernardino County	Individual	648 E GLEN OAK ST, , RIALTO, CA 923766648	1760500664	F	103T00000X
2	Colorado Springs	113	80903	Colorado	CO	{"x": -104.81990027499995, "y": 38.84235050400...	El Paso County	Individual	224 E WILLAMETTE AVE, , COLORADO SPRINGS, CO 8...	1023011046	M	103T00000X
3	Miami	118	33134	Florida	FL	{"x": -80.26657097299994, "y": 25.771643515000...	Miami-Dade County	Organization	4343 W FLAGLER ST STE 100, , CORAL GABLES, FL ...	1942507769	None	2084P0800X
4	Tamuning	122	96913	Guam	GU	{"x": 144.7754183520001, "y": 13.5051031310000...		Individual	790 GOV. CARLOS G. CAMACHO RD., , TAMUNING, GU...	1508862269	M	2084P0800X

	RegionAbbr	Provider_Count	OBJECTID	SHAPE	STATE_NAME	ST_ABBREV	TOTPOP_CY
0	CA	51820	5	{'rings': [[[-13038833.5744, 3845968.519900001...	California	CA	39806791
1	NY	23808	33	{'rings': [[[-8259979.4246, 4949745.592100002]...	New York	NY	20070143
2	FL	22890	10	{'rings': [[[-9124037.3451, 2817150.9723999985...	Florida	FL	20875686
3	MI	18107	23	{'rings': [[[-9291127.6781, 5121002.034400001]...	Michigan	MI	10057191
4	TX	13623	44	{'rings': [[[-10822386.4159, 2999262.775600001...	Texas	TX	28954616

	RegionAbbr	Provider_Count	OBJECTID	SHAPE	STATE_NAME	ST_ABBREV	TOTPOP_CY	people_per_prov
43	MS	1108	25	{'rings': [[[-9952997.0803, 3545020.2216000035...	Mississippi	MS	3051594	2754.15
33	AL	2030	1	{'rings': [[[-9747504.6398, 3539549.5786999986...	Alabama	AL	4968383	2447.48
37	IA	1396	16	{'rings': [[[-10154309.0285, 5388473.678900003...	Iowa	IA	3219046	2305.91
45	ID	790	13	{'rings': [[[-12362299.9589, 5539778.200800002...	Idaho	ID	1760131	2228.01
4	TX	13623	44	{'rings': [[[-10822386.4159, 2999262.775600001...	Texas	TX	28954616	2125.42

	ALAND	AWATER	B13016_001E	B13016_001M	B13016_002E	B13016_002M	B13016_003E	B13016_003M	B13016_004E	B13016_004M	B13016_005E	B13016_005M	B13016_006E	B13016_006M	B13016_007E	B13016_007M	B13016_008E	B13016_008M	B13016_009M	B13016_010E	B13016_010M	B13016_011E	B13016_011M	B13016_012E	B13016_012M	B13016_013E	B13016_013M	B13016_014E	B13016_014M	B13016_015E	B13016_015M	B13016_016E	B13016_016M	B13016_017E	B13016_017M	B13016_calc_num15to19E	B13016_calc_num15to19M	B13016_calc_num20to24E	B13016_calc_num20to24M	B13016_calc_num25to29E	B13016_calc_num25to29M	B13016_calc_num30to34E	B13016_calc_num30to34M	B13016_calc_num35to39E	B13016_calc_num35to39M	B13016_calc_num40to44E	B13016_calc_num40to44M	B13016_calc_num45to50E	B13016_calc_num45to50M	B13016_calc_pct15to19E	B13016_calc_pct15to19M	B13016_calc_pct20to24E	B13016_calc_pct20to24M	B13016_calc_pct25to29E	B13016_calc_pct25to29M	B13016_calc_pct30to34E	B13016_calc_pct30to34M	B13016_calc_pct35to39E	B13016_calc_pct35to39M	B13016_calc_pct40to44E	B13016_calc_pct40to44M	B13016_calc_pct45to50M	B13016_calc_pctBirthsE	B13016_calc_pctBirthsM	COUNTYNS	GEOID	NAME	OBJECTID	SHAPE	Shape__Area	Shape__Length	State
0	2557960600	2833854	446	58	19	20	12	17	0	9	0	9	0	9	7	11	0	9	9	427	65	37	24	67	29	53	24	56	21	48	23	103	37	63	31	49	29	67	30	53	26	56	23	55	25	103	38	63	32	24.5	31.521266	0.0	13.432836	0.0	16.981132	0.0	16.071429	12.7	19.145034	0.0	8.737864	14.285714	4.3	4.480234	01034210	38001	Adams County	1991	{"rings": [[[-11354354.1023, 5813340.1041], [-...	5.324157e+09	330639.744151	North Dakota
1	3863107435	56591279	2234	110	173	73	0	15	5	10	124	63	18	22	26	35	0	15	15	2061	127	391	64	317	79	216	70	267	62	238	73	274	53	358	48	391	66	322	80	340	94	285	66	264	81	274	55	358	50	0.0	3.836317	1.6	3.081535	36.5	15.545781	6.3	7.579469	9.8	12.908628	0.0	5.474453	4.189944	7.7	3.257869	01034225	38003	Barnes County	1992	{"rings": [[[-10904992.8409, 5981421.7966], [-...	8.403601e+09	374027.679170	North Dakota
2	3596568808	131708144	1390	16	128	33	8	9	68	24	20	13	18	11	7	6	7	7	13	1262	37	202	21	181	25	178	13	166	12	155	24	140	25	240	16	210	23	249	35	198	18	184	16	162	25	147	26	240	21	3.8	4.265356	27.3	8.841183	10.1	6.501124	9.8	5.917430	4.3	3.643182	4.8	4.686829	5.416667	9.2	2.364013	01034216	38005	Benson County	1993	{"rings": [[[-11075502.1995, 6168791.7355], [-...	8.343651e+09	537806.413896	North Dakota
3	2975481852	11990484	174	36	5	5	0	9	0	9	3	4	0	9	2	3	0	9	9	169	35	20	19	12	11	10	10	33	15	32	13	14	11	48	16	20	21	12	14	13	11	33	17	34	13	14	14	48	18	0.0	45.000000	0.0	75.000000	23.1	23.779327	0.0	27.272727	5.9	8.532061	0.0	64.285714	18.750000	2.9	2.872377	01035616	38007	Billings County	1994	{"rings": [[[-11476951.3137, 5995895.8254], [-...	6.425825e+09	369444.155012	North Dakota
4	4321196493	74864998	1204	49	68	43	0	13	11	14	16	14	24	29	17	21	0	13	13	1136	67	163	37	159	43	117	15	141	27	175	49	132	37	249	42	163	39	170	45	133	21	165	40	192	53	132	39	249	44	0.0	7.975460	6.5	8.055208	12.0	10.353515	14.5	17.218402	8.9	10.660919	0.0	9.848485	5.220884	5.6	3.565728	01034227	38009	Bottineau County	1995	{"rings": [[[-11152289.0833, 6274727.6819], [-...	1.011984e+10	483713.596470	North Dakota

	SHAPE	addnumfrom	addnumto	addr_type	addressnumber	addressrange	city	country	displayx	displayy	extrainfo	in_address	in_address2	in_city	in_postal	in_region	langcode	loc_name	longlabel	match_addr	neighborhood	objectid	place_addr	postal	rank	region	regionabbr	score	shortlabel	side	staddr	status	stdir	stname	stpredir	sttype	subregion	user_address	user_address2	user_city	user_country	user_entity_type	user_full_address	user_npi	user_organization_name	user_postal	user_provider_gender	user_region	user_taxonomy_code_1	user_taxonomy_code_10	user_taxonomy_code_11	user_taxonomy_code_12	user_taxonomy_code_13	user_taxonomy_code_14	user_taxonomy_code_15	user_taxonomy_code_2	user_taxonomy_code_3	user_taxonomy_code_4	user_taxonomy_code_5	user_taxonomy_code_6	user_taxonomy_code_7	user_taxonomy_code_8	user_taxonomy_code_9	user_taxonomy_group_1	x	x_max	x_min	y	y_max	y_min
0	{"x": -96.78767519099995, "y": 46.884720309000...	801	825	StreetAddress	801	801-825	Fargo	USA	-96.787675	46.884720		801 BROADWAY N	None	FARGO	58122.0	ND	ENG	World	801 Broadway N, Fargo, ND, 58102, USA	801 Broadway N, Fargo, North Dakota, 58102	Downtown Fargo	3720	801 Broadway N, Fargo, North Dakota, 58102	58102	20.0	North Dakota	ND	99.51	801 Broadway N	R	801 Broadway N	M	N	Broadway			Cass County	801 BROADWAY N	None	FARGO	US	Individual	801 BROADWAY N, , FARGO, ND 58122	1.851398e+09	None	58122.0	M	ND	207VM0101X	None	None	None	None	None	None	207VM0101X	None	None	None	None	None	None	None	None	-96.787675	-96.786675	-96.788675	46.884720	46.885720	46.883720
1	{"x": -97.06651079899996, "y": 47.912072701000...	1098	1000	StreetAddress	1000	1000-1098	Grand Forks	USA	-97.066511	47.912073	582066002	1000 SOUTH COLUMBIA ROAD	None	GRAND FORKS	582066002.0	ND	ENG	World	1000 S Columbia Rd, Grand Forks, ND, 58201, USA	1000 S Columbia Rd, Grand Forks, North Dakota,...		12480	1000 S Columbia Rd, Grand Forks, North Dakota,...	58201	20.0	North Dakota	ND	97.86	1000 S Columbia Rd	L	1000 S Columbia Rd	M		Columbia	S	Rd	Grand Forks County	1000 SOUTH COLUMBIA ROAD	None	GRAND FORKS	US	Individual	1000 SOUTH COLUMBIA ROAD, , GRAND FORKS, ND 58...	1.366440e+09	None	582066002.0	M	ND	207V00000X	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	-97.066511	-97.065511	-97.067511	47.912073	47.913073	47.911073
2	{"x": -99.74991768599995, "y": 48.838085857000...	1	1099	StreetAddress	1	1-1099	Belcourt	USA	-99.749918	48.838086		1 HOSPITAL ROAD NORTH	None	BELCOURT	58316.0	ND	ENG	World	1 Hospital Rd, Belcourt, ND, 58316, USA	1 Hospital Rd, Belcourt, North Dakota, 58316		45476	1 Hospital Rd, Belcourt, North Dakota, 58316	58316	20.0	North Dakota	ND	98.87	1 Hospital Rd	L	1 Hospital Rd	M		Hospital		Rd	Rolette County	1 HOSPITAL ROAD NORTH	None	BELCOURT	US	Individual	1 HOSPITAL ROAD NORTH, , BELCOURT, ND 58316	1.164496e+09	None	58316.0	M	ND	207V00000X	None	None	None	None	None	None	207V00000X	None	None	None	None	None	None	None	None	-99.749918	-99.748918	-99.750918	48.838086	48.839086	48.837086
3	{"x": -100.77651963899996, "y": 46.80847046400...			PointAddress	1000		Bismarck	USA	-100.776510	46.808793		1000 E ROSSER AVE	None	BISMARCK	585014414.0	ND	ENG	World	1000 E Rosser Ave, Bismarck, ND, 58501, USA	1000 E Rosser Ave, Bismarck, North Dakota, 58501	Downtown Bismarck	60149	1000 E Rosser Ave, Bismarck, North Dakota, 58501	58501	20.0	North Dakota	ND	100.00	1000 E Rosser Ave	R	1000 E Rosser Ave	M		Rosser	E	Ave	Burleigh County	1000 E ROSSER AVE	None	BISMARCK	US	Individual	1000 E ROSSER AVE, , BISMARCK, ND 585014414	1.982658e+09	None	585014414.0	M	ND	207V00000X	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	-100.776520	-100.775510	-100.777510	46.808470	46.809793	46.807793
4	{"x": -100.77651963899996, "y": 46.80847046400...			PointAddress	1000		Bismarck	USA	-100.776510	46.808793		1000 E ROSSER AVE	None	BISMARCK	585014414.0	ND	ENG	World	1000 E Rosser Ave, Bismarck, ND, 58501, USA	1000 E Rosser Ave, Bismarck, North Dakota, 58501	Downtown Bismarck	60879	1000 E Rosser Ave, Bismarck, North Dakota, 58501	58501	20.0	North Dakota	ND	100.00	1000 E Rosser Ave	R	1000 E Rosser Ave	M		Rosser	E	Ave	Burleigh County	1000 E ROSSER AVE	None	BISMARCK	US	Individual	1000 E ROSSER AVE, , BISMARCK, ND 585014414	1.093769e+09	None	585014414.0	M	ND	207V00000X	None	None	None	None	None	None	None	None	None	None	None	None	None	None	None	-100.776520	-100.775510	-100.777510	46.808470	46.809793	46.807793

	Name	Provider_Count	Total Women (15 to 50)	Women who had birth (past 12 months)	women_per_prov	mother_per_prov
38	Richland County	1	3459	231	3459	231
46	Stutsman County	2	4275	303	2137.5	151.5
44	Stark County	5	6850	592	1370	118.4
50	Ward County	10	16362	1141	1636.2	114.1
52	Williams County	8	7258	779	907.25	97.375
8	Cass County	36	44821	2936	1245.03	81.5556
42	Sioux County	1	1056	75	1056	75
17	Grand Forks County	13	17889	895	1376.08	68.8462
39	Rolette County	3	3262	171	1087.33	57
7	Burleigh County	22	20934	1213	951.545	55.1364
29	Morton County	NaN	6575	463	NaN	NaN
1	Barnes County	NaN	2234	173	NaN	NaN
26	McKenzie County	NaN	2528	150	NaN	NaN
10	Dickey County	NaN	1026	149	NaN	NaN
24	McHenry County	NaN	1128	137	NaN	NaN
33	Pembina County	NaN	1206	129	NaN	NaN
2	Benson County	NaN	1390	128	NaN	NaN
49	Walsh County	NaN	1982	120	NaN	NaN
35	Ramsey County	NaN	2372	117	NaN	NaN
30	Mountrail County	NaN	2078	113	NaN	NaN
27	McLean County	NaN	1654	109	NaN	NaN
28	Mercer County	NaN	1612	98	NaN	NaN
5	Bowman County	NaN	660	90	NaN	NaN
51	Wells County	NaN	612	84	NaN	NaN
34	Pierce County	NaN	773	76	NaN	NaN
4	Bottineau County	NaN	1204	68	NaN	NaN
11	Divide County	NaN	396	65	NaN	NaN
20	Hettinger County	NaN	533	64	NaN	NaN
12	Dunn County	NaN	828	60	NaN	NaN
13	Eddy County	NaN	420	57	NaN	NaN
48	Traill County	NaN	1637	57	NaN	NaN
15	Foster County	NaN	631	53	NaN	NaN
36	Ransom County	NaN	1057	52	NaN	NaN
19	Griggs County	NaN	391	50	NaN	NaN
9	Cavalier County	NaN	597	35	NaN	NaN
47	Towner County	NaN	341	35	NaN	NaN
16	Golden Valley County	NaN	368	34	NaN	NaN
37	Renville County	NaN	517	34	NaN	NaN
40	Sargent County	NaN	735	32	NaN	NaN
32	Oliver County	NaN	306	31	NaN	NaN
21	Kidder County	NaN	426	30	NaN	NaN
31	Nelson County	NaN	461	28	NaN	NaN
14	Emmons County	NaN	570	26	NaN	NaN
6	Burke County	NaN	403	22	NaN	NaN
22	LaMoure County	NaN	691	20	NaN	NaN
0	Adams County	NaN	446	19	NaN	NaN
23	Logan County	NaN	275	15	NaN	NaN
18	Grant County	NaN	390	14	NaN	NaN
41	Sheridan County	NaN	224	14	NaN	NaN
45	Steele County	NaN	293	10	NaN	NaN
25	McIntosh County	NaN	407	7	NaN	NaN
3	Billings County	NaN	174	5	NaN	NaN
43	Slope County	NaN	75	1	NaN	NaN

	regionabbr	subregion	Provider_Count
100	CA	Los Angeles County	2094
37	IL	Cook County	1586
1167	NY	New York County	1136
9	TX	Harris County	1124
134	AZ	Maricopa County	844

	regionabbr	subregion	Provider_Count	AAGEBASECY	AGGDI_CY	AGGHINC_CY	AGGINC_CY	AGGNW_CY	AIFBASE_CY	AIMBASE_CY	AMERIND_CY	AREA	ASIAN_CY	ASNFBASECY	ASNMBASECY	ASSCDEG_CY	AVGDI_CY	AVGFMSZ_CY	AVGHHSZ_CY	AVGHINC_CY	AVGNW_CY	AVGVAL_CY	BABYBOOMCY	BACHDEG_CY	BAGEBASECY	BLACK_CY	BLKFBASECY	BLKMBASECY	CIVLBFR_CY	EDUCBASECY	EMP_CY	FAMHH_CY	FAMPOP_CY	FEM0_CY	FEM15_CY	FEM18UP_CY	FEM20_CY	FEM21UP_CY	FEM25_CY	FEM30_CY	FEM35_CY	FEM40_CY	FEM45_CY	FEM50_CY	FEM55_CY	FEM5_CY	FEM60_CY	FEM65_CY	FEM70_CY	FEM75_CY	FEM80_CY	FEM85_CY	FEMALES_CY	GED_CY	GENALPHACY	GENBASE_CY	GENX_CY	GENZ_CY	GQPOP_CY	GRADDEG_CY	HAGEBASECY	HHPOP_CY	HINC0_CY	HINC150_CY	HINC15_CY	HINC25_CY	HINC35_CY	HINC50_CY	HINC75_CY	HINCBASECY	HISPAI_CY	HISPASN_CY	HISPBLK_CY	HISPMLT_CY	HISPOTH_CY	HISPPI_CY	HISPPOP_CY	HISPWHT_CY	HSGRAD_CY	HSPFBASECY	HSPMBASECY	IAGEBASECY	ID	LANDAREA	MAL18UP_CY	MAL21UP_CY	MALE0_CY	MALE15_CY	MALE20_CY	MALE25_CY	MALE30_CY	MALE35_CY	MALE40_CY	MALE45_CY	MALE50_CY	MALE55_CY	MALE5_CY	MALE60_CY	MALE65_CY	MALE70_CY	MALE75_CY	MALE80_CY	MALE85_CY	MALES_CY	MARBASE_CY	MARRIED_CY	MEDAGE_CY	MEDDI_CY	MEDFAGE_CY	MEDHHR_CY	MEDHINC_CY	MEDMAGE_CY	MEDNW_CY	MEDVAL_CY	MILLENN_CY	MINORITYCY	MLTFBASECY	MLTMBASECY	NAME	NEVMARR_CY	NHSPAI_CY	NHSPASN_CY	NHSPBLK_CY	NHSPMLT_CY	NHSPOTH_CY	NHSPPI_CY	NHSPWHT_CY	NOHS_CY	NONHISP_CY	OAGEBASECY	OBJECTID	OLDRGENSCY	OTHFBASECY	OTHMBASECY	OTHRACE_CY	OWNER_CY	PACIFIC_CY	PAGEBASECY	PCI_CY	PIFBASE_CY	PIMBASE_CY	POP0_CY	POP15_CY	POP18UP_CY	POP20_CY	POP21UP_CY	POP25_CY	POP30_CY	POP35_CY	POP40_CY	POP45_CY	POP50_CY	POP55_CY	POP5_CY	POP60_CY	POP65_CY	POP70_CY	POP75_CY	POP80_CY	POP85_CY	POPDENS_CY	RACE2UP_CY	RACEBASECY	RENTER_CY	SHAPE	SMCOLL_CY	SOMEHS_CY	STATE_NAME	ST_ABBREV	Shape_Area	Shape_Length	TLIFECODE	TLIFENAME	TOTHH_CY	TOTHU_CY	TOTPOP_CY	UNEMPRT_CY	UNEMP_CY	VACANT_CY	WAGEBASECY	WHITE_CY	WHTFBASECY	WHTMBASECY	WIDOWED_CY
0	CA	Los Angeles County	2094	1517296.0	2.359152e+11	3.196410e+11	3.247511e+11	2.224411e+12	35957.0	37602.0	73559.0	4085.699440	1517296.0	805865.0	711431.0	469649.0	70013.0	3.63	3.00	94861.0	660145.0	710686.0	1987624.0	1445895.0	850350.0	850350.0	450243.0	400107.0	5232399.0	6899087.0	4995839.0	2281483.0	8292844.0	312953.0	331081.0	4077284.0	384255.0	3858162.0	421330.0	392960.0	359612.0	328513.0	335961.0	332084.0	327978.0	310887.0	291733.0	246493.0	183490.0	131256.0	90178.0	110499.0	5208957.0	120351.0	255399.0	10288937.0	2130083.0	2353920.0	176419.0	782758.0	5043293.0	10112518.0	371190.0	243110.0	310104.0	280437.0	390334.0	554983.0	394431.0	3369582.0	54769.0	22824.0	45013.0	261906.0	2280356.0	3826.0	5043293.0	2374599.0	1288396.0	2522627.0	2520666.0	73559.0	06037	4057.8835	3890494.0	3663128.0	326230.0	351424.0	396276.0	433863.0	402148.0	368194.0	325765.0	331204.0	323486.0	307396.0	325231.0	261725.0	209710.0	149559.0	102402.0	65741.0	65807.0	5079980.0	8362123.0	3752436.0	35.7	52454.0	36.9	50.6	62751.0	34.6	60024.0	569360.0	2875966.0	7644196.0	251940.0	244185.0	Los Angeles County	3521307.0	18790.0	1494472.0	805337.0	234219.0	24674.0	23411.0	2644741.0	878838.0	5245644.0	2305030.0	205.0	685945.0	1141774.0	1163256.0	2305030.0	1538585.0	27237.0	27237.0	31563.0	13746.0	13491.0	639183.0	682505.0	7967778.0	780531.0	7521290.0	855193.0	795108.0	727806.0	654278.0	667165.0	655570.0	635374.0	636118.0	553458.0	456203.0	333049.0	233658.0	155919.0	176306.0	2535.5	496125.0	10288937.0	1831065.0	{'rings': [[[-13201886.423700001, 3900000.9334...	1300786.0	612414.0	California	CA	1.553815e+10	9.723835e+05	13	Next Wave	3369650.0	3576478.0	10288937.0	4.5	236560.0	206828.0	5019340.0	5019340.0	2509432.0	2509908.0	413837.0
1	IL	Cook County	1586	395712.0	1.284204e+11	1.771502e+11	1.797564e+11	1.326345e+12	10418.0	10695.0	21113.0	953.145682	395712.0	206041.0	189671.0	237028.0	63978.0	3.36	2.58	88254.0	660769.0	335311.0	1076607.0	830433.0	1233716.0	1233716.0	674544.0	559172.0	2791109.0	3607345.0	2599303.0	1216270.0	4087547.0	158205.0	161652.0	2144742.0	185272.0	2040962.0	217698.0	207243.0	188312.0	166222.0	164629.0	166570.0	174115.0	159117.0	162326.0	139839.0	104629.0	75554.0	54048.0	70522.0	2717277.0	95959.0	129327.0	5274129.0	1058747.0	1171989.0	90942.0	552050.0	1374256.0	5183187.0	250519.0	132865.0	186804.0	169815.0	238778.0	329118.0	243670.0	2007274.0	15131.0	4083.0	22733.0	66348.0	601231.0	706.0	1374256.0	664024.0	726645.0	671160.0	703096.0	21113.0	17031	945.3262	1963911.0	1860397.0	163128.0	166591.0	180196.0	214748.0	206465.0	188502.0	162787.0	160501.0	157805.0	158061.0	164098.0	141495.0	116039.0	82196.0	55154.0	35924.0	35961.0	2556852.0	4301056.0	1851961.0	36.6	47971.0	37.9	50.8	59718.0	35.4	76719.0	256912.0	1442885.0	3078944.0	78362.0	75557.0	Cook County	1813320.0	5982.0	391629.0	1210983.0	87571.0	7441.0	1082.0	2195185.0	237082.0	3899873.0	608672.0	611.0	394574.0	294742.0	313930.0	608672.0	1108536.0	1788.0	1788.0	34083.0	870.0	918.0	321333.0	328243.0	4108653.0	365468.0	3901359.0	432446.0	413708.0	376814.0	329009.0	325130.0	324375.0	332176.0	323215.0	303821.0	255878.0	186825.0	130708.0	89972.0	106483.0	5579.2	153919.0	5274129.0	898740.0	{'rings': [[[-9743209.5443, 5109696.7809000015...	690225.0	237923.0	Illinois	IL	4.445620e+09	7.424352e+05	3	Uptown Individuals	2007276.0	2230295.0	5274129.0	6.9	191806.0	223019.0	2859209.0	2859209.0	1452300.0	1406909.0	250105.0
2	NY	New York County	1136	212844.0	6.672674e+10	1.091191e+11	1.109282e+11	4.008776e+11	4542.0	4214.0	8756.0	22.950686	212844.0	119067.0	93777.0	48360.0	83690.0	3.02	2.00	136860.0	502791.0	1307852.0	345825.0	406878.0	247085.0	247085.0	133471.0	113614.0	996167.0	1251653.0	956908.0	314952.0	951099.0	35672.0	40691.0	760316.0	71461.0	725473.0	95217.0	86134.0	68890.0	53980.0	50374.0	50312.0	53400.0	32553.0	52503.0	49261.0	38526.0	27262.0	18025.0	22645.0	878322.0	26610.0	30855.0	1660472.0	339808.0	248875.0	66726.0	371255.0	441304.0	1593746.0	107793.0	70466.0	58555.0	49858.0	60209.0	94133.0	78323.0	797304.0	6592.0	2097.0	43951.0	36560.0	187987.0	415.0	441304.0	163702.0	125099.0	233374.0	207930.0	8756.0	36061	22.8287	660480.0	632378.0	37014.0	36186.0	57628.0	82593.0	79654.0	67405.0	53997.0	51176.0	48844.0	48003.0	33820.0	43057.0	37266.0	28231.0	20234.0	12716.0	11948.0	782150.0	1457619.0	517739.0	37.9	61201.0	38.3	48.2	82611.0	37.4	47793.0	1216203.0	557913.0	899778.0	40103.0	33176.0	New York County	745585.0	2164.0	210747.0	203134.0	36719.0	5204.0	506.0	760694.0	80622.0	1219168.0	193191.0	1859.0	137196.0	101593.0	91598.0	193191.0	185125.0	921.0	921.0	66805.0	484.0	437.0	72686.0	76877.0	1420796.0	129089.0	1357851.0	177810.0	165788.0	136295.0	107977.0	101550.0	99156.0	101403.0	66373.0	95560.0	86527.0	66757.0	47496.0	30741.0	34593.0	72736.2	73279.0	1660472.0	612187.0	{'rings': [[[-8242549.350199999, 4966646.20400...	121070.0	71759.0	New York	NY	1.024044e+08	1.085936e+05	3	Uptown Individuals	797312.0	890327.0	1660472.0	3.9	39259.0	93015.0	924396.0	924396.0	479062.0	445334.0	68402.0
3	TX	Harris County	1124	341640.0	1.132993e+11	1.470200e+11	1.487312e+11	1.000460e+12	14364.0	15637.0	30001.0	1748.811066	341640.0	173439.0	168201.0	193511.0	68781.0	3.49	2.85	89252.0	607353.0	251600.0	848003.0	605813.0	901459.0	901459.0	478023.0	423436.0	2335018.0	3029538.0	2196764.0	1124808.0	3920902.0	176809.0	152219.0	1773341.0	169057.0	1680385.0	196592.0	183051.0	171499.0	154056.0	148493.0	141554.0	141858.0	172608.0	125458.0	102481.0	70111.0	46215.0	29690.0	32228.0	2380573.0	98795.0	145111.0	4735852.0	961416.0	1219972.0	49092.0	345886.0	2035551.0	4686760.0	161383.0	106787.0	153424.0	159410.0	212729.0	290829.0	190407.0	1647247.0	21351.0	3892.0	27998.0	100351.0	703910.0	850.0	2035551.0	1177199.0	608836.0	994167.0	1041384.0	30001.0	48201	1703.4776	1723465.0	1625451.0	182838.0	161982.0	172174.0	206139.0	192088.0	176799.0	153424.0	146647.0	137454.0	132895.0	178822.0	114553.0	90812.0	60215.0	36665.0	20887.0	17674.0	2355279.0	3684970.0	1809576.0	33.5	50339.0	34.3	47.5	59417.0	32.7	72820.0	176533.0	1332331.0	3339578.0	85910.0	87501.0	Harris County	1363826.0	8650.0	337748.0	873461.0	73060.0	8500.0	2608.0	1396274.0	316268.0	2700301.0	712410.0	2624.0	229019.0	342097.0	370313.0	712410.0	888580.0	3458.0	3458.0	31405.0	1768.0	1690.0	359647.0	314201.0	3496806.0	341231.0	3305836.0	402731.0	375139.0	348298.0	307480.0	295140.0	279008.0	274753.0	351430.0	240011.0	193293.0	130326.0	82880.0	50577.0	49902.0	2780.1	173411.0	4735852.0	758671.0	{'rings': [[[-10584325.232, 3445368.621600002]...	606842.0	253587.0	Texas	TX	5.991513e+09	8.173476e+05	7	Ethnic Enclaves	1647251.0	1790697.0	4735852.0	5.9	138254.0	143446.0	2573473.0	2573473.0	1284972.0	1288501.0	161546.0
4	AZ	Maricopa County	844	184150.0	1.043304e+11	1.333034e+11	1.346919e+11	1.310493e+12	51929.0	46655.0	98584.0	9224.040205	184150.0	97892.0	86258.0	241714.0	64964.0	3.32	2.69	83005.0	816017.0	303705.0	872897.0	597653.0	253576.0	253576.0	124641.0	128935.0	2181523.0	2891837.0	2065771.0	1053223.0	3492063.0	149041.0	138470.0	1694648.0	151115.0	1606050.0	165009.0	153091.0	145864.0	134160.0	133211.0	130665.0	133428.0	147853.0	125740.0	117630.0	90474.0	62955.0	42348.0	51162.0	2217638.0	95969.0	119899.0	4387226.0	855726.0	1066649.0	64540.0	342159.0	1373153.0	4322686.0	163449.0	98771.0	135427.0	144542.0	215958.0	297023.0	213940.0	1605964.0	23948.0	4716.0	16585.0	73969.0	592016.0	1314.0	1373153.0	660605.0	548954.0	682683.0	690470.0	98584.0	04013	9200.1431	1624795.0	1531223.0	154750.0	146575.0	157726.0	172645.0	157924.0	148689.0	134272.0	132380.0	127236.0	123794.0	153320.0	111456.0	101867.0	78340.0	52720.0	33351.0	31426.0	2169588.0	3485723.0	1676208.0	35.8	50661.0	37.0	50.4	59691.0	34.7	102641.0	237947.0	1136843.0	1981147.0	89351.0	87304.0	Maricopa County	1200935.0	74636.0	179434.0	236991.0	102686.0	6019.0	8228.0	2406079.0	160973.0	3014073.0	598035.0	104.0	335212.0	292917.0	305118.0	598035.0	998231.0	9542.0	9542.0	30701.0	4646.0	4896.0	303791.0	285045.0	3319443.0	308841.0	3137273.0	337654.0	311015.0	294553.0	268432.0	265591.0	257901.0	257222.0	301173.0	237196.0	219497.0	168814.0	115675.0	75699.0	82588.0	476.9	176655.0	4387226.0	607760.0	{'rings': [[[-12494641.7694, 4017468.270400002...	711033.0	193382.0	Arizona	AZ	3.420485e+10	1.142138e+06	7	Ethnic Enclaves	1605991.0	1813056.0	4387226.0	5.3	115752.0	207065.0	3066684.0	3066684.0	1556262.0	1510422.0	182502.0

	regionabbr	subregion	Provider_Count	AAGEBASECY	AGGDI_CY	AGGHINC_CY	AGGINC_CY	AGGNW_CY	AIFBASE_CY	AIMBASE_CY	AMERIND_CY	AREA	ASIAN_CY	ASNFBASECY	ASNMBASECY	ASSCDEG_CY	AVGDI_CY	AVGFMSZ_CY	AVGHHSZ_CY	AVGHINC_CY	AVGNW_CY	AVGVAL_CY	BABYBOOMCY	BACHDEG_CY	BAGEBASECY	BLACK_CY	BLKFBASECY	BLKMBASECY	CIVLBFR_CY	EDUCBASECY	EMP_CY	FAMHH_CY	FAMPOP_CY	FEM0_CY	FEM15_CY	FEM18UP_CY	FEM20_CY	FEM21UP_CY	FEM25_CY	FEM30_CY	FEM35_CY	FEM40_CY	FEM45_CY	FEM50_CY	FEM55_CY	FEM5_CY	FEM60_CY	FEM65_CY	FEM70_CY	FEM75_CY	FEM80_CY	FEM85_CY	FEMALES_CY	GED_CY	GENALPHACY	GENBASE_CY	GENX_CY	GENZ_CY	GRADDEG_CY	HAGEBASECY	HHPOP_CY	HINC0_CY	HINC150_CY	HINC15_CY	HINC25_CY	HINC35_CY	HINC50_CY	HINC75_CY	HINCBASECY	HISPMLT_CY	HISPOTH_CY	HISPPOP_CY	HISPWHT_CY	HSGRAD_CY	HSPFBASECY	HSPMBASECY	IAGEBASECY	LANDAREA	MAL18UP_CY	MAL21UP_CY	MALE0_CY	MALE15_CY	MALE20_CY	MALE25_CY	MALE30_CY	MALE35_CY	MALE40_CY	MALE45_CY	MALE50_CY	MALE55_CY	MALE5_CY	MALE60_CY	MALE65_CY	MALE70_CY	MALE75_CY	MALE80_CY	MALE85_CY	MALES_CY	MARBASE_CY	MARRIED_CY	MEDAGE_CY	MEDDI_CY	MEDFAGE_CY	MEDHHR_CY	MEDHINC_CY	MEDMAGE_CY	MEDNW_CY	MEDVAL_CY	MILLENN_CY	MINORITYCY	MLTFBASECY	MLTMBASECY	NEVMARR_CY	NHSPAI_CY	NHSPASN_CY	NHSPBLK_CY	NHSPMLT_CY	NHSPWHT_CY	NOHS_CY	NONHISP_CY	OAGEBASECY	OLDRGENSCY	OTHFBASECY	OTHRACE_CY	OWNER_CY	PCI_CY	POP0_CY	POP15_CY	POP18UP_CY	POP20_CY	POP21UP_CY	POP25_CY	POP30_CY	POP35_CY	POP40_CY	POP45_CY	POP50_CY	POP55_CY	POP5_CY	POP60_CY	POP65_CY	POP70_CY	POP75_CY	POP80_CY	POP85_CY	POPDENS_CY	RACE2UP_CY	RACEBASECY	RENTER_CY	SMCOLL_CY	SOMEHS_CY	TOTHH_CY	TOTHU_CY	TOTPOP_CY	UNEMPRT_CY	UNEMP_CY	VACANT_CY	WAGEBASECY	WHITE_CY	WHTFBASECY	WHTMBASECY	WIDOWED_CY
0	CA	Los Angeles County	2094.0	1517296.0	2.359152e+11	3.196410e+11	3.247511e+11	2.224411e+12	35957.0	37602.0	73559.0	4085.699440	1517296.0	805865.0	711431.0	469649.0	70013.0	3.63	3.00	94861.0	660145.0	710686.0	1987624.0	1445895.0	850350.0	850350.0	450243.0	400107.0	5232399.0	6899087.0	4995839.0	2281483.0	8292844.0	312953.0	331081.0	4077284.0	384255.0	3858162.0	421330.0	392960.0	359612.0	328513.0	335961.0	332084.0	327978.0	310887.0	291733.0	246493.0	183490.0	131256.0	90178.0	110499.0	5208957.0	120351.0	255399.0	10288937.0	2130083.0	2353920.0	782758.0	5043293.0	10112518.0	371190.0	243110.0	310104.0	280437.0	390334.0	554983.0	394431.0	3369582.0	261906.0	2280356.0	5043293.0	2374599.0	1288396.0	2522627.0	2520666.0	73559.0	4057.8835	3890494.0	3663128.0	326230.0	351424.0	396276.0	433863.0	402148.0	368194.0	325765.0	331204.0	323486.0	307396.0	325231.0	261725.0	209710.0	149559.0	102402.0	65741.0	65807.0	5079980.0	8362123.0	3752436.0	35.7	52454.0	36.9	50.6	62751.0	34.6	60024.0	569360.0	2875966.0	7644196.0	251940.0	244185.0	3521307.0	18790.0	1494472.0	805337.0	234219.0	2644741.0	878838.0	5245644.0	2305030.0	685945.0	1141774.0	2305030.0	1538585.0	31563.0	639183.0	682505.0	7967778.0	780531.0	7521290.0	855193.0	795108.0	727806.0	654278.0	667165.0	655570.0	635374.0	636118.0	553458.0	456203.0	333049.0	233658.0	155919.0	176306.0	2535.5	496125.0	10288937.0	1831065.0	1300786.0	612414.0	3369650.0	3576478.0	10288937.0	4.5	236560.0	206828.0	5019340.0	5019340.0	2509432.0	2509908.0	413837.0
1	IL	Cook County	1586.0	395712.0	1.284204e+11	1.771502e+11	1.797564e+11	1.326345e+12	10418.0	10695.0	21113.0	953.145682	395712.0	206041.0	189671.0	237028.0	63978.0	3.36	2.58	88254.0	660769.0	335311.0	1076607.0	830433.0	1233716.0	1233716.0	674544.0	559172.0	2791109.0	3607345.0	2599303.0	1216270.0	4087547.0	158205.0	161652.0	2144742.0	185272.0	2040962.0	217698.0	207243.0	188312.0	166222.0	164629.0	166570.0	174115.0	159117.0	162326.0	139839.0	104629.0	75554.0	54048.0	70522.0	2717277.0	95959.0	129327.0	5274129.0	1058747.0	1171989.0	552050.0	1374256.0	5183187.0	250519.0	132865.0	186804.0	169815.0	238778.0	329118.0	243670.0	2007274.0	66348.0	601231.0	1374256.0	664024.0	726645.0	671160.0	703096.0	21113.0	945.3262	1963911.0	1860397.0	163128.0	166591.0	180196.0	214748.0	206465.0	188502.0	162787.0	160501.0	157805.0	158061.0	164098.0	141495.0	116039.0	82196.0	55154.0	35924.0	35961.0	2556852.0	4301056.0	1851961.0	36.6	47971.0	37.9	50.8	59718.0	35.4	76719.0	256912.0	1442885.0	3078944.0	78362.0	75557.0	1813320.0	5982.0	391629.0	1210983.0	87571.0	2195185.0	237082.0	3899873.0	608672.0	394574.0	294742.0	608672.0	1108536.0	34083.0	321333.0	328243.0	4108653.0	365468.0	3901359.0	432446.0	413708.0	376814.0	329009.0	325130.0	324375.0	332176.0	323215.0	303821.0	255878.0	186825.0	130708.0	89972.0	106483.0	5579.2	153919.0	5274129.0	898740.0	690225.0	237923.0	2007276.0	2230295.0	5274129.0	6.9	191806.0	223019.0	2859209.0	2859209.0	1452300.0	1406909.0	250105.0
2	NY	New York County	1136.0	212844.0	6.672674e+10	1.091191e+11	1.109282e+11	4.008776e+11	4542.0	4214.0	8756.0	22.950686	212844.0	119067.0	93777.0	48360.0	83690.0	3.02	2.00	136860.0	502791.0	1307852.0	345825.0	406878.0	247085.0	247085.0	133471.0	113614.0	996167.0	1251653.0	956908.0	314952.0	951099.0	35672.0	40691.0	760316.0	71461.0	725473.0	95217.0	86134.0	68890.0	53980.0	50374.0	50312.0	53400.0	32553.0	52503.0	49261.0	38526.0	27262.0	18025.0	22645.0	878322.0	26610.0	30855.0	1660472.0	339808.0	248875.0	371255.0	441304.0	1593746.0	107793.0	70466.0	58555.0	49858.0	60209.0	94133.0	78323.0	797304.0	36560.0	187987.0	441304.0	163702.0	125099.0	233374.0	207930.0	8756.0	22.8287	660480.0	632378.0	37014.0	36186.0	57628.0	82593.0	79654.0	67405.0	53997.0	51176.0	48844.0	48003.0	33820.0	43057.0	37266.0	28231.0	20234.0	12716.0	11948.0	782150.0	1457619.0	517739.0	37.9	61201.0	38.3	48.2	82611.0	37.4	47793.0	1216203.0	557913.0	899778.0	40103.0	33176.0	745585.0	2164.0	210747.0	203134.0	36719.0	760694.0	80622.0	1219168.0	193191.0	137196.0	101593.0	193191.0	185125.0	66805.0	72686.0	76877.0	1420796.0	129089.0	1357851.0	177810.0	165788.0	136295.0	107977.0	101550.0	99156.0	101403.0	66373.0	95560.0	86527.0	66757.0	47496.0	30741.0	34593.0	72736.2	73279.0	1660472.0	612187.0	121070.0	71759.0	797312.0	890327.0	1660472.0	3.9	39259.0	93015.0	924396.0	924396.0	479062.0	445334.0	68402.0
3	TX	Harris County	1124.0	341640.0	1.132993e+11	1.470200e+11	1.487312e+11	1.000460e+12	14364.0	15637.0	30001.0	1748.811066	341640.0	173439.0	168201.0	193511.0	68781.0	3.49	2.85	89252.0	607353.0	251600.0	848003.0	605813.0	901459.0	901459.0	478023.0	423436.0	2335018.0	3029538.0	2196764.0	1124808.0	3920902.0	176809.0	152219.0	1773341.0	169057.0	1680385.0	196592.0	183051.0	171499.0	154056.0	148493.0	141554.0	141858.0	172608.0	125458.0	102481.0	70111.0	46215.0	29690.0	32228.0	2380573.0	98795.0	145111.0	4735852.0	961416.0	1219972.0	345886.0	2035551.0	4686760.0	161383.0	106787.0	153424.0	159410.0	212729.0	290829.0	190407.0	1647247.0	100351.0	703910.0	2035551.0	1177199.0	608836.0	994167.0	1041384.0	30001.0	1703.4776	1723465.0	1625451.0	182838.0	161982.0	172174.0	206139.0	192088.0	176799.0	153424.0	146647.0	137454.0	132895.0	178822.0	114553.0	90812.0	60215.0	36665.0	20887.0	17674.0	2355279.0	3684970.0	1809576.0	33.5	50339.0	34.3	47.5	59417.0	32.7	72820.0	176533.0	1332331.0	3339578.0	85910.0	87501.0	1363826.0	8650.0	337748.0	873461.0	73060.0	1396274.0	316268.0	2700301.0	712410.0	229019.0	342097.0	712410.0	888580.0	31405.0	359647.0	314201.0	3496806.0	341231.0	3305836.0	402731.0	375139.0	348298.0	307480.0	295140.0	279008.0	274753.0	351430.0	240011.0	193293.0	130326.0	82880.0	50577.0	49902.0	2780.1	173411.0	4735852.0	758671.0	606842.0	253587.0	1647251.0	1790697.0	4735852.0	5.9	138254.0	143446.0	2573473.0	2573473.0	1284972.0	1288501.0	161546.0
4	AZ	Maricopa County	844.0	184150.0	1.043304e+11	1.333034e+11	1.346919e+11	1.310493e+12	51929.0	46655.0	98584.0	9224.040205	184150.0	97892.0	86258.0	241714.0	64964.0	3.32	2.69	83005.0	816017.0	303705.0	872897.0	597653.0	253576.0	253576.0	124641.0	128935.0	2181523.0	2891837.0	2065771.0	1053223.0	3492063.0	149041.0	138470.0	1694648.0	151115.0	1606050.0	165009.0	153091.0	145864.0	134160.0	133211.0	130665.0	133428.0	147853.0	125740.0	117630.0	90474.0	62955.0	42348.0	51162.0	2217638.0	95969.0	119899.0	4387226.0	855726.0	1066649.0	342159.0	1373153.0	4322686.0	163449.0	98771.0	135427.0	144542.0	215958.0	297023.0	213940.0	1605964.0	73969.0	592016.0	1373153.0	660605.0	548954.0	682683.0	690470.0	98584.0	9200.1431	1624795.0	1531223.0	154750.0	146575.0	157726.0	172645.0	157924.0	148689.0	134272.0	132380.0	127236.0	123794.0	153320.0	111456.0	101867.0	78340.0	52720.0	33351.0	31426.0	2169588.0	3485723.0	1676208.0	35.8	50661.0	37.0	50.4	59691.0	34.7	102641.0	237947.0	1136843.0	1981147.0	89351.0	87304.0	1200935.0	74636.0	179434.0	236991.0	102686.0	2406079.0	160973.0	3014073.0	598035.0	335212.0	292917.0	598035.0	998231.0	30701.0	303791.0	285045.0	3319443.0	308841.0	3137273.0	337654.0	311015.0	294553.0	268432.0	265591.0	257901.0	257222.0	301173.0	237196.0	219497.0	168814.0	115675.0	75699.0	82588.0	476.9	176655.0	4387226.0	607760.0	711033.0	193382.0	1605991.0	1813056.0	4387226.0	5.3	115752.0	207065.0	3066684.0	3066684.0	1556262.0	1510422.0	182502.0

	regionabbr	Provider_Count	ALAND	AWATER	B13016_001E	B13016_001M	B13016_002E	B13016_002M	B13016_003E	B13016_003M	B13016_004E	B13016_004M	B13016_005E	B13016_005M	B13016_006E	B13016_006M	B13016_007E	B13016_007M	B13016_008E	B13016_008M	B13016_009E	B13016_009M	B13016_010E	B13016_010M	B13016_011E	B13016_011M	B13016_012E	B13016_012M	B13016_013E	B13016_013M	B13016_014E	B13016_014M	B13016_015E	B13016_015M	B13016_016E	B13016_016M	B13016_017E	B13016_017M	B13016_calc_num15to19E	B13016_calc_num15to19M	B13016_calc_num20to24E	B13016_calc_num20to24M	B13016_calc_num25to29E	B13016_calc_num25to29M	B13016_calc_num30to34E	B13016_calc_num30to34M	B13016_calc_num35to39E	B13016_calc_num35to39M	B13016_calc_num40to44E	B13016_calc_num40to44M	B13016_calc_num45to50E	B13016_calc_num45to50M	B13016_calc_pct15to19E	B13016_calc_pct15to19M	B13016_calc_pct20to24E	B13016_calc_pct20to24M	B13016_calc_pct25to29E	B13016_calc_pct25to29M	B13016_calc_pct30to34E	B13016_calc_pct30to34M	B13016_calc_pct35to39E	B13016_calc_pct35to39M	B13016_calc_pct40to44E	B13016_calc_pct40to44M	B13016_calc_pct45to50E	B13016_calc_pct45to50M	B13016_calc_pctBirthsE	B13016_calc_pctBirthsM	GEOID	NAME	OBJECTID	SHAPE	STATENS	STUSPS	Shape__Area	Shape__Length	women_per_prov	mother_per_prov
51	ND	102	178710423375	4400396641	168792	563	11410	783	517	161	2609	459	3726	398	2880	363	1411	359	211	79	56	39	157382	957	22304	415	28639	582	22947	498	21004	401	19288	789	18822	685	24378	456	22821	445	31248	741	26673	638	23884	541	20699	867	19033	690	24434	458	2.3	0.704106	8.3	1.455489	14.0	1.454254	12.1	1.495102	6.8	1.710719	1.1	0.413118	0.2	0.159556	6.8	0.462823	38	North Dakota	35	{'rings': [[[-10823452.8858, 6274958.149], [-1...	01779797	ND	4.003510e+11	3.123593e+06	1654.82	111.863
32	UT	526	212884846341	7000199770	744748	957	52639	1428	1263	256	10387	710	16697	866	15192	712	7502	531	1267	267	331	133	692109	1759	112478	509	112858	819	92431	832	94332	748	98897	1460	87738	1541	93375	840	113741	570	123245	1084	109128	1201	109524	1033	106399	1554	89005	1564	93706	850	1.1	0.225004	8.4	0.571299	15.3	0.775493	13.9	0.636786	7.1	0.488325	1.4	0.298938	0.4	0.141897	7.1	0.191263	49	Utah	45	{'rings': [[[-12139394.6091, 5012444.5138], [-...	01455989	UT	3.682861e+11	2.557183e+06	1415.87	100.074
35	AR	440	134768100673	2963631791	689530	1063	40901	1606	2514	379	11224	869	12726	754	8852	676	4012	546	1088	240	485	148	648629	1983	95325	724	89233	1111	86020	996	86174	898	88668	1665	91183	1443	112026	960	97839	817	100457	1410	98746	1249	95026	1124	92680	1752	92271	1463	112511	971	2.6	0.386776	11.2	0.850713	12.9	0.745972	9.3	0.702799	4.3	0.583413	1.2	0.259431	0.4	0.131490	5.9	0.232502	05	Arkansas	4	{'rings': [[[-9989043.0946, 4300705.9503], [-9...	00068085	AR	2.053261e+11	2.656502e+06	1567.11	92.9568
49	SD	137	196345228871	3384471850	186754	651	12697	717	518	138	2940	319	3849	413	3346	358	1581	259	326	121	137	75	174057	958	26460	431	26086	538	23172	488	23773	446	23415	710	22287	652	28864	519	26978	453	29026	625	27021	639	27119	572	24996	756	22613	663	29001	524	1.9	0.510511	10.1	1.077157	14.2	1.490858	12.3	1.294202	6.3	1.018354	1.4	0.533418	0.5	0.258471	6.8	0.383039	46	South Dakota	42	{'rings': [[[-10749419.778, 5769979.5139], [-1...	01785534	SD	3.919820e+11	2.962929e+06	1363.17	92.6788
43	ID	250	214048160737	2393355752	376343	997	22877	960	810	230	5132	512	7280	635	6059	590	2698	402	614	197	284	118	353466	1221	56534	619	48332	641	45833	703	47302	723	49003	1372	48670	1344	57792	862	57344	660	53464	820	53113	947	53361	933	51701	1430	49284	1358	58076	870	1.4	0.400759	9.6	0.946269	13.7	1.170320	11.4	1.087706	5.2	0.764034	1.2	0.398247	0.5	0.203050	6.1	0.254615	16	Idaho	13	{'rings': [[[-12918526.7316, 6275006.0277], [-...	01779783	ID	4.246748e+11	4.197354e+06	1505.37	91.508
34	IA	480	144664158135	1081293682	701974	1000	41743	1604	1689	243	8619	825	12653	687	11690	657	5427	509	1084	202	581	153	660231	1735	102593	513	102818	1014	81504	832	85354	784	87584	1311	87550	1343	112828	900	104282	568	111437	1307	94157	1079	97044	1023	93011	1406	88634	1358	113409	913	1.6	0.232855	7.7	0.734750	13.4	0.713196	12.0	0.664997	5.8	0.540092	1.2	0.227132	0.5	0.134847	5.9	0.228094	19	Iowa	16	{'rings': [[[-10154309.3595, 5388474.8922], [-...	01779785	IA	2.648094e+11	2.527131e+06	1462.45	86.9646
38	NE	336	198957965731	1370523694	432593	701	27898	932	730	171	5463	461	8225	523	8611	542	3585	354	814	147	470	140	404695	1042	62040	499	61378	681	52108	580	54877	587	54908	1025	53435	1002	65949	606	62770	527	66841	822	60333	781	63488	799	58493	1084	54249	1013	66419	622	1.2	0.272248	8.2	0.682333	13.6	0.848703	13.6	0.836466	6.1	0.594447	1.5	0.269520	0.7	0.210679	6.4	0.214997	31	Nebraska	28	{'rings': [[[-10736262.9814, 5234760.2573], [-...	01779792	NE	3.578638e+11	2.922099e+06	1287.48	83.0298
33	KS	486	211753159949	1346325556	662453	1061	39414	1429	1687	279	8906	730	10899	674	11480	653	4970	483	1053	181	419	116	623039	1549	95455	743	93872	976	81579	911	83346	770	82588	1355	82948	1302	103251	848	97142	794	102778	1219	92478	1133	94826	1010	87558	1439	84001	1315	103670	856	1.7	0.286857	8.7	0.702794	11.8	0.714376	12.1	0.676449	5.7	0.543689	1.3	0.214578	0.4	0.111844	5.9	0.215331	20	Kansas	17	{'rings': [[[-10609671.1704, 4865943.0191], [-...	00481813	KS	3.484442e+11	2.570743e+06	1363.07	81.0988
28	OK	668	177664450004	3373087228	904535	884	53590	1422	2937	286	14103	760	15571	816	12508	702	6145	454	1550	222	776	154	850945	1533	122870	616	122845	799	117785	877	119076	803	111910	1381	117202	1377	139257	887	125807	679	136948	1103	133356	1198	131584	1067	118055	1454	118752	1395	140033	900	2.3	0.226983	10.3	0.548722	11.7	0.602838	9.5	0.527902	5.2	0.379185	1.3	0.186314	0.6	0.109916	5.9	0.156932	40	Oklahoma	37	{'rings': [[[-10532824.3805, 4438955.0818], [-...	01102857	OK	2.744190e+11	3.307886e+06	1354.09	80.2246
24	MN	932	206229176104	18944967530	1268872	1143	73295	1732	2101	293	11193	595	19565	807	25081	941	11248	605	3000	324	1107	210	1195577	1943	172421	645	167175	694	159438	903	164182	998	159460	2115	160590	2161	212311	1075	174522	708	178368	914	179003	1211	189263	1372	170708	2200	163590	2185	213418	1095	1.2	0.167816	6.3	0.332027	10.9	0.444725	13.3	0.487823	6.6	0.344083	1.8	0.196536	0.5	0.098362	5.8	0.136271	27	Minnesota	24	{'rings': [[[-10243096.0782, 5894294.6155], [-...	00662849	MN	4.587742e+11	4.504564e+06	1361.45	78.6427
1	TX	5161	676641930188	19017521093	6800021	2997	397530	5500	21768	1195	86594	2398	109234	2557	104129	2933	54463	1770	16076	926	5266	497	6402491	6428	930609	1869	872706	2713	878353	2601	878663	3357	876401	5195	907688	5045	1058071	2829	952377	2218	959300	3621	987587	3647	982792	4458	930864	5488	923764	5129	1063337	2872	2.3	0.125363	9.0	0.247641	11.1	0.255672	10.6	0.294540	5.9	0.186991	1.7	0.099775	0.5	0.046721	5.8	0.080744	48	Texas	44	{'rings': [[[-10867517.6923, 2998334.6597], [-...	01779801	TX	9.487726e+11	8.498835e+06	1317.58	77.0258
23	WI	934	140275464079	29359527964	1312355	1160	67517	1511	1782	296	12926	751	18740	762	21201	784	9297	582	2589	320	982	188	1244838	1825	185801	643	185287	805	155085	871	161462	863	163691	1828	166713	1668	226799	1102	187583	708	198213	1101	173825	1157	182663	1166	172988	1918	169302	1698	227781	1118	0.9	0.157756	6.5	0.377150	10.8	0.432459	11.6	0.422763	5.4	0.331121	1.5	0.188388	0.4	0.082508	5.1	0.114984	55	Wisconsin	50	{'rings': [[[-9739359.7332, 5630191.737], [-97...	01779806	WI	2.874128e+11	3.531267e+06	1405.09	72.288
30	MS	530	121530256608	3930182746	714792	1225	37818	1534	2383	361	10517	674	11817	764	7666	635	3786	422	1000	222	649	206	676974	2173	102332	1294	95368	1337	88387	878	89783	809	91519	1561	93499	1668	116086	1046	104715	1343	105885	1497	100204	1164	97449	1028	95305	1617	94499	1683	116735	1066	2.3	0.343508	9.9	0.620857	11.8	0.750037	7.9	0.646317	4.0	0.437629	1.1	0.234166	0.6	0.176395	5.3	0.214307	28	Mississippi	25	{'rings': [[[-9847958.7008, 3529611.841], [-98...	01779790	MS	1.752353e+11	3.003227e+06	1348.66	71.3547
26	AL	831	131174431216	4592944701	1150523	1402	58732	1690	2903	415	14484	792	18605	889	13911	884	6318	680	1790	423	721	193	1091791	1867	157636	1096	152944	1175	144825	1120	141620	1125	147991	2099	152552	2185	194223	1366	160539	1172	167428	1417	163430	1430	155531	1431	154309	2206	154342	2226	194944	1380	1.8	0.258167	8.7	0.467339	11.4	0.534766	8.9	0.562386	4.1	0.436770	1.2	0.273556	0.4	0.098968	5.1	0.146698	01	Alabama	1	{'rings': [[[-9805779.6231, 3536997.5778], [-9...	01779775	AL	1.901226e+11	2.312416e+06	1384.5	70.6763
14	WA	1304	172111800165	12560067439	1700267	1488	92040	2034	2729	392	15476	1058	25580	1155	28015	975	15442	943	3270	360	1528	268	1608227	2552	211659	736	216767	1102	230636	1166	226631	980	221964	2666	221665	2536	278905	1447	214388	834	232243	1528	256216	1641	254646	1382	237406	2828	224935	2561	280433	1472	1.3	0.182779	6.7	0.453443	10.0	0.446233	11.0	0.378200	6.5	0.389580	1.5	0.159188	0.5	0.095524	5.4	0.119453	53	Washington	48	{'rings': [[[-13651702.4638, 5966299.4185], [-...	01779804	WA	3.831213e+11	5.712577e+06	1303.89	70.5828
47	AK	155	1478588231566	277723861311	174163	485	10582	662	408	125	2240	276	3190	359	2763	326	1483	270	430	99	68	40	163581	789	22202	326	22666	352	25803	411	23760	384	21123	710	21134	779	26893	421	22610	349	24906	447	28993	546	26523	504	22606	760	21564	785	26961	423	1.8	0.552151	9.0	1.096348	11.0	1.220770	10.4	1.213076	6.6	1.173833	2.0	0.453323	0.3	0.148310	6.1	0.379401	02	Alaska	2	{'rings': [[[-19937057.4487, 6661392.0514], [-...	01785533	AK	8.208934e+12	6.399688e+07	1123.63	68.271
18	IN	1226	92787765193	1539541677	1552714	1801	83517	2136	3805	417	19285	1076	23972	1055	22734	1091	9433	681	3196	366	1092	204	1469197	2803	220438	998	215755	1343	189329	1208	187767	1234	197144	2121	201249	2184	257515	1595	224243	1082	235040	1721	213301	1604	210501	1647	206577	2228	204445	2214	258607	1608	1.7	0.185779	8.2	0.453835	11.2	0.487332	10.8	0.511352	4.6	0.325960	1.6	0.178219	0.4	0.078840	5.4	0.137366	18	Indiana	15	{'rings': [[[-9440558.1637, 5115568.7167], [-9...	00448508	IN	1.595776e+11	2.230325e+06	1266.49	68.1215
16	AZ	1257	294198661567	1027245114	1571003	1527	85196	2315	3290	346	18209	982	24282	1113	22146	1110	12496	754	3273	324	1500	303	1485807	2427	222499	969	213748	1310	201156	1099	196251	1121	196215	2313	204729	2415	251209	1483	225789	1029	231957	1637	225438	1564	218397	1578	208711	2433	208002	2437	252709	1514	1.5	0.153096	7.9	0.419714	10.8	0.488018	10.1	0.502940	6.0	0.354459	1.6	0.154673	0.6	0.119848	5.4	0.147141	04	Arizona	3	{'rings': [[[-12138854.2127, 4438965.0002], [-...	01779777	AZ	4.340581e+11	2.906214e+06	1249.8	67.7772
31	NV	530	284329327067	2047350803	688113	933	35640	1268	1283	226	6223	592	10631	732	9530	608	5524	525	1890	307	559	152	652473	1553	84103	463	84252	577	93345	773	91894	702	89600	1600	94203	1543	115076	842	85386	515	90475	827	103976	1065	101424	929	95124	1684	96093	1573	115635	856	1.5	0.264525	6.9	0.651297	10.2	0.696176	9.4	0.593253	5.8	0.542252	2.0	0.317856	0.5	0.131399	5.2	0.184025	32	Nevada	29	{'rings': [[[-12695040.5225, 5160062.591], [-1...	01779793	NV	4.802969e+11	3.056062e+06	1298.33	67.2453
50	WY	110	251465641446	1860628076	129212	681	7365	602	375	126	1544	305	2356	304	2043	304	671	159	182	91	194	128	121847	800	17167	389	17492	455	16875	469	17498	370	17010	743	16377	706	19428	396	17542	409	19036	548	19231	559	19541	479	17681	760	16559	712	19622	416	2.1	0.716545	8.1	1.585122	12.3	1.540148	10.5	1.534449	3.8	0.884351	1.1	0.547514	1.0	0.651992	5.7	0.465144	56	Wyoming	51	{'rings': [[[-11583671.286, 5621145.5785], [-1...	01779807	WY	4.741976e+11	2.777154e+06	1174.65	66.9545
25	KY	838	102266092821	2388731561	1030561	1405	55402	1750	3028	399	13166	857	16961	1022	13590	776	6097	564	1600	249	960	204	975159	2193	138223	959	135326	1058	126513	1129	126042	899	132526	2176	138214	2165	178315	1333	141251	1039	148492	1362	143474	1523	139632	1188	138623	2248	139814	2179	179275	1349	2.1	0.282035	8.9	0.571377	11.8	0.701184	9.7	0.549543	4.4	0.400558	1.1	0.177198	0.5	0.113720	5.4	0.169565	21	Kentucky	18	{'rings': [[[-9961457.8678, 4369322.1272], [-9...	01779786	KY	1.667122e+11	2.753667e+06	1229.79	66.1122
17	TN	1247	106798015774	2354836197	1568704	1693	80791	1690	4526	524	20304	905	23108	1028	20814	977	8840	612	2353	352	846	179	1487913	2450	201325	1026	205419	1273	203950	1157	195684	1206	202771	2291	210101	2246	268663	1487	205851	1152	225723	1562	227058	1548	216498	1552	211611	2371	212454	2273	269509	1498	2.2	0.254255	9.0	0.396073	10.2	0.447400	9.6	0.445981	4.2	0.285397	1.1	0.165259	0.3	0.066394	5.2	0.107589	47	Tennessee	43	{'rings': [[[-9314702.8266, 4383607.3303], [-9...	01325873	TN	1.664791e+11	2.621414e+06	1257.98	64.7883
36	NM	410	314191415563	733669653	473337	885	26525	1011	1668	320	6652	660	7358	493	6006	493	3241	368	1069	264	531	147	446812	1265	66249	597	65036	722	60420	529	61432	543	57947	1392	60040	1326	75688	914	67917	677	71688	978	67778	723	67438	733	61188	1440	61109	1352	76219	926	2.5	0.470527	9.3	0.911912	10.9	0.718097	8.9	0.724605	5.3	0.588365	1.7	0.430278	0.7	0.192679	5.6	0.213254	35	New Mexico	32	{'rings': [[[-11466167.8568, 4439128.5521], [-...	00897535	NM	4.644013e+11	2.900272e+06	1154.48	64.6951
22	LA	950	111904803121	23746413153	1118915	1237	60549	1822	3272	477	14926	787	18139	1093	15227	1015	6641	592	1463	224	881	239	1058366	2319	143686	845	152617	1005	153462	1154	149438	1227	140645	1983	140142	2076	178376	1105	146958	970	167543	1276	171601	1589	164665	1592	147286	2069	141605	2088	179257	1131	2.2	0.324250	8.9	0.464804	10.6	0.629377	9.2	0.609885	4.5	0.396917	1.0	0.157451	0.5	0.133292	5.4	0.162598	22	Louisiana	19	{'rings': [[[-10120798.2502, 3381965.1428], [-...	01629543	LA	1.675793e+11	4.198750e+06	1177.81	63.7358
39	WV	310	62265662566	489840834	402606	842	19682	946	962	193	5270	530	6147	516	4683	490	1758	262	474	136	388	133	382924	1448	52229	589	54109	734	47390	658	48286	560	52550	1252	56801	1174	71559	710	53191	620	59379	905	53537	836	52969	744	54308	1279	57275	1182	71947	722	1.8	0.362230	8.9	0.882262	11.5	0.946996	8.8	0.916697	3.2	0.476372	0.8	0.236836	0.5	0.184779	4.9	0.234688	54	West Virginia	49	{'rings': [[[-8847301.8894, 4825493.6305], [-8...	01779805	WV	1.030460e+11	2.614073e+06	1298.73	63.4903
0	CA	7543	403483182192	20484637928	9642845	3558	478458	5207	16079	831	76861	2027	120625	2303	138924	3037	87689	2301	28439	1168	9841	670	9164387	6546	1258668	1367	1302552	2268	1324982	2293	1245434	3141	1207288	5298	1256240	5262	1569223	3493	1274747	1600	1379413	3042	1445607	3250	1384358	4369	1294977	5776	1284679	5390	1579064	3557	1.3	0.065170	5.6	0.146432	8.3	0.158202	10.0	0.217082	6.8	0.175101	2.2	0.090442	0.6	0.042407	5.0	0.053932	06	California	5	{'rings': [[[-13198544.1255, 3897778.6019], [-...	01779778	CA	6.500881e+11	5.274852e+06	1278.38	63.4307
45	ME	226	79885808421	11748168571	289915	724	14100	799	336	101	2505	379	3953	437	4730	494	1795	266	566	163	215	83	275815	1171	39289	411	35635	529	34673	431	33882	553	35424	773	40593	827	56319	669	39625	423	38140	651	38626	614	38612	742	37219	817	41159	843	56534	674	0.8	0.254729	6.6	0.987364	10.2	1.119605	12.3	1.257551	4.8	0.706804	1.4	0.395022	0.4	0.146744	4.9	0.275272	23	Maine	20	{'rings': [[[-7859306.2593, 5309094.2568], [-7...	01779787	ME	1.723955e+11	3.775975e+06	1282.81	62.3894
7	OH	2303	105833282399	10264451012	2667220	1781	141773	2541	6130	524	30094	1354	42334	1574	38506	1191	18285	966	4320	414	2104	282	2525447	2850	371578	986	355390	1582	334827	1619	322728	1436	327887	2534	352679	2623	460358	1495	377708	1117	385484	2082	377161	2258	361234	1866	346172	2712	356999	2655	462462	1521	1.6	0.138648	7.8	0.348707	11.2	0.411883	10.7	0.325073	5.3	0.275967	1.2	0.115617	0.5	0.060960	5.3	0.095133	39	Ohio	36	{'rings': [[[-9206145.5154, 5103064.4505], [-9...	01085497	OH	1.842500e+11	2.145534e+06	1158.15	61.5601
9	GA	2221	149177524294	4733385577	2555460	2228	136101	2966	6976	689	29979	1407	37397	1708	34265	1749	19692	1085	5484	537	2308	359	2419359	3653	342872	1735	321174	1978	319620	1873	318105	2028	332176	4063	351277	3863	434135	2063	349848	1867	351153	2427	357017	2535	352370	2678	351868	4205	356761	3900	436443	2094	2.0	0.196655	8.5	0.396312	10.5	0.472592	9.7	0.490821	5.6	0.301014	1.5	0.149580	0.5	0.082217	5.3	0.115900	13	Georgia	11	{'rings': [[[-9251608.9138, 4163971.617], [-92...	01705317	GA	2.159941e+11	2.582918e+06	1150.59	61.2792
11	VA	1752	102254240128	8532012665	2030818	1520	105067	2534	3410	426	17561	983	27855	1074	31174	1228	17769	907	5313	488	1985	313	1925751	3050	265171	1434	264803	1720	261294	1297	255919	1537	257606	2676	270990	2501	349968	1427	268581	1496	282364	1981	289149	1684	287093	1967	275375	2826	276303	2548	351953	1461	1.3	0.158454	6.2	0.345387	9.6	0.367173	10.9	0.421216	6.5	0.322644	1.9	0.175725	0.6	0.088902	5.2	0.124610	51	Virginia	47	{'rings': [[[-8458295.369, 4552089.1276], [-84...	01779803	VA	1.673934e+11	3.720225e+06	1159.14	59.9697
20	SC	1036	77862731483	5071381300	1148289	1334	61725	1553	2488	355	14262	843	18116	777	15574	923	7993	718	2375	329	917	217	1086564	2135	154271	955	150761	1107	148504	927	140833	1038	144940	1977	150681	2119	196574	1223	156759	1019	165023	1391	166620	1210	156407	1389	152933	2103	153056	2144	197491	1242	1.6	0.226227	8.6	0.505617	10.9	0.459598	10.0	0.583464	5.2	0.463953	1.6	0.213852	0.5	0.109840	5.4	0.135049	45	South Carolina	41	{'rings': [[[-8743152.9102, 4008829.0132], [-8...	01779799	SC	1.174042e+11	1.913047e+06	1108.39	59.5801
46	MT	204	376960880222	3871059371	222821	664	12139	662	446	128	2584	368	3740	390	3240	385	1548	221	436	125	145	68	210682	1068	30452	541	32190	552	27591	537	28702	523	29153	756	26963	792	35631	515	30898	556	34774	663	31331	664	31942	649	30701	788	27399	802	35776	519	1.4	0.413451	7.4	1.048736	11.9	1.218795	10.1	1.187559	5.0	0.708117	1.6	0.453837	0.4	0.189981	5.4	0.296658	30	Montana	27	{'rings': [[[-11582648.3446, 6274793.6263], [-...	00767982	MT	8.201726e+11	4.615725e+06	1092.26	59.5049
15	MO	1302	178052037005	2488190402	1407252	1622	76296	1712	3489	389	16646	972	23356	861	20049	911	8871	645	2828	361	1057	261	1330956	2415	190626	918	191885	1286	179620	1021	179655	1020	176549	2090	178696	2054	233925	1282	194115	997	208531	1612	202976	1336	199704	1368	185420	2187	181524	2085	234982	1308	1.8	0.200184	8.0	0.462015	11.5	0.417372	10.0	0.450962	4.8	0.343251	1.6	0.198065	0.4	0.111044	5.4	0.121477	29	Missouri	26	{'rings': [[[-10176764.2505, 4921064.8725], [-...	01779791	MO	2.943633e+11	3.050106e+06	1080.84	58.5991
19	CO	1219	268425964573	1178495763	1314134	1296	69064	1764	2404	332	12043	820	17412	919	21424	1119	11618	808	3189	355	974	206	1245070	2249	166276	836	166510	986	181129	1036	177232	1246	173695	2203	169594	1954	210634	1163	168680	900	178553	1282	198541	1385	198656	1675	185313	2347	172783	1986	211608	1181	1.4	0.196675	6.7	0.456687	8.8	0.458816	10.8	0.555897	6.3	0.428728	1.8	0.204362	0.5	0.097316	5.3	0.134047	08	Colorado	6	{'rings': [[[-11583155.812, 5012550.0328], [-1...	01779779	CO	4.473109e+11	2.708282e+06	1078.04	56.6563
27	OR	829	248604328809	6195045325	938000	1156	46891	1423	1699	316	8315	710	13570	701	13080	764	6930	560	2496	369	801	240	891109	1807	117922	763	120981	968	123026	871	126495	855	128059	1931	123396	1959	151230	1153	119621	826	129296	1200	136596	1118	139575	1147	134989	2011	125892	1993	152031	1178	1.4	0.263986	6.4	0.545874	9.9	0.506710	9.4	0.541931	5.1	0.407738	2.0	0.291423	0.5	0.157810	5.0	0.151516	41	Oregon	38	{'rings': [[[-13739902.4519, 5817179.7538], [-...	01155107	OR	4.851724e+11	3.245438e+06	1131.48	56.5633
10	NC	2192	125919712692	13470113896	2418330	2156	121971	2930	5235	508	25688	1407	35174	1474	31407	1317	16816	958	5491	572	2160	397	2296359	3841	319881	1312	312107	1528	304942	1555	296291	1484	306747	3124	336614	3168	419777	2197	325116	1407	337795	2077	340116	2143	327698	1984	323563	3268	342105	3219	421937	2233	1.6	0.156096	7.6	0.413892	10.3	0.428455	9.6	0.397683	5.2	0.291388	1.6	0.166517	0.5	0.094051	5.0	0.121004	37	North Carolina	34	{'rings': [[[-8404865.2607, 4207455.9158], [-8...	01027616	NC	1.958019e+11	4.403645e+06	1103.25	55.6437
37	HI	358	16634117742	11777681513	321374	647	19748	968	520	152	3061	386	5958	525	5351	462	3364	407	1019	212	475	139	301626	1224	37171	191	40913	397	43264	528	43662	490	40864	945	41945	915	53807	691	37691	244	43974	554	49222	745	49013	673	44228	1029	42964	939	54282	705	1.4	0.403180	7.0	0.873400	12.1	1.050744	10.9	0.930610	7.6	0.903056	2.4	0.490706	0.9	0.255818	6.1	0.300637	15	Hawaii	12	{'rings': [[[-17321095.4151, 2287790.5412], [-...	01779782	HI	1.909508e+10	1.616132e+06	897.693	55.162
5	IL	2917	143784114293	6211277447	3091828	1949	156704	2888	5474	474	26960	1227	39116	1577	48078	1371	26498	1004	8103	604	2475	308	2935124	3924	412694	870	409396	1285	402723	1548	393800	1493	394748	2875	403151	2957	518612	1857	418168	991	436356	1777	441839	2210	441878	2027	421246	3045	411254	3018	521087	1882	1.3	0.113309	6.2	0.280064	8.9	0.354160	10.9	0.306226	6.3	0.233963	2.0	0.146154	0.5	0.059082	5.1	0.093287	17	Illinois	14	{'rings': [[[-9773905.0437, 5234955.0855], [-9...	01779784	IL	2.496541e+11	2.747059e+06	1059.93	53.7209
42	NH	286	23187445452	1028643155	301067	670	15210	832	371	128	2319	360	3922	424	5066	439	2536	350	690	176	306	118	285857	1081	42986	354	41316	566	34004	474	33273	475	35445	1015	40155	1045	58678	634	43357	376	43635	671	37926	636	38339	647	37981	1074	40845	1060	58984	645	0.9	0.295130	5.3	0.820968	10.3	1.104435	13.2	1.123125	6.7	0.901964	1.7	0.428661	0.5	0.199974	5.1	0.275998	33	New Hampshire	30	{'rings': [[[-7860509.7182, 5307853.9016], [-7...	01779794	NH	4.599331e+10	1.325759e+06	1052.68	53.1818
3	FL	4170	138911437206	31398800291	4568521	3282	220509	4001	7831	683	40098	1713	62132	1887	59055	1976	35502	1738	11662	912	4229	457	4348012	4727	571732	1277	592706	1972	595319	2008	570329	2166	576543	4501	619527	4380	821856	3083	579563	1448	632804	2612	657451	2756	629384	2932	612045	4825	631189	4474	826085	3117	1.4	0.117799	6.3	0.269433	9.5	0.284270	9.4	0.310900	5.8	0.280260	1.8	0.143894	0.5	0.055287	4.8	0.087476	12	Florida	10	{'rings': [[[-9139900.2618, 2825608.9535], [-9...	00294478	FL	1.961979e+11	4.679537e+06	1095.57	52.8799

	regionabbr	Provider_Count
0	CA	7543
1	TX	5161
2	NY	5054
3	FL	4170
4	PA	3283

	RegionAbbr	Provider_Count	AAGEBASECY	AGGDI_CY	AGGHINC_CY	AGGINC_CY	AGGNW_CY	AIFBASE_CY	AIMBASE_CY	AMERIND_CY	AREA	ASIAN_CY	ASNFBASECY	ASNMBASECY	ASSCDEG_CY	AVGDI_CY	AVGFMSZ_CY	AVGHHSZ_CY	AVGHINC_CY	AVGNW_CY	AVGVAL_CY	BABYBOOMCY	BACHDEG_CY	BAGEBASECY	BLACK_CY	BLKFBASECY	BLKMBASECY	CIVLBFR_CY	EDUCBASECY	EMP_CY	FAMHH_CY	FAMPOP_CY	FEM0_CY	FEM15_CY	FEM18UP_CY	FEM20_CY	FEM21UP_CY	FEM25_CY	FEM30_CY	FEM35_CY	FEM40_CY	FEM45_CY	FEM50_CY	FEM55_CY	FEM5_CY	FEM60_CY	FEM65_CY	FEM70_CY	FEM75_CY	FEM80_CY	FEM85_CY	FEMALES_CY	GED_CY	GENALPHACY	GENBASE_CY	GENX_CY	GENZ_CY	GQPOP_CY	GRADDEG_CY	HAGEBASECY	HHPOP_CY	HINC0_CY	HINC150_CY	HINC15_CY	HINC25_CY	HINC35_CY	HINC50_CY	HINC75_CY	HINCBASECY	HISPAI_CY	HISPASN_CY	HISPBLK_CY	HISPMLT_CY	HISPOTH_CY	HISPPI_CY	HISPPOP_CY	HISPWHT_CY	HSGRAD_CY	HSPFBASECY	HSPMBASECY	IAGEBASECY	ID	LANDAREA	MAL18UP_CY	MAL21UP_CY	MALE0_CY	MALE15_CY	MALE20_CY	MALE25_CY	MALE30_CY	MALE35_CY	MALE40_CY	MALE45_CY	MALE50_CY	MALE55_CY	MALE5_CY	MALE60_CY	MALE65_CY	MALE70_CY	MALE75_CY	MALE80_CY	MALE85_CY	MALES_CY	MARBASE_CY	MARRIED_CY	MEDAGE_CY	MEDDI_CY	MEDFAGE_CY	MEDHHR_CY	MEDHINC_CY	MEDMAGE_CY	MEDNW_CY	MEDVAL_CY	MILLENN_CY	MINORITYCY	MLTFBASECY	MLTMBASECY	NAME	NEVMARR_CY	NHSPAI_CY	NHSPASN_CY	NHSPBLK_CY	NHSPMLT_CY	NHSPOTH_CY	NHSPPI_CY	NHSPWHT_CY	NOHS_CY	NONHISP_CY	OAGEBASECY	OBJECTID	OLDRGENSCY	OTHFBASECY	OTHMBASECY	OTHRACE_CY	OWNER_CY	PACIFIC_CY	PAGEBASECY	PCI_CY	PIFBASE_CY	PIMBASE_CY	POP0_CY	POP15_CY	POP18UP_CY	POP20_CY	POP21UP_CY	POP25_CY	POP30_CY	POP35_CY	POP40_CY	POP45_CY	POP50_CY	POP55_CY	POP5_CY	POP60_CY	POP65_CY	POP70_CY	POP75_CY	POP80_CY	POP85_CY	POPDENS_CY	RACE2UP_CY	RACEBASECY	RENTER_CY	SHAPE	SMCOLL_CY	SOMEHS_CY	STATE_NAME	ST_ABBREV	Shape_Area	Shape_Length	TADULT01	TADULT02	TADULT03	TADULT04	TADULT05	TADULT06	TADULT07	TADULT08	TADULT09	TADULT11	TADULT12	TADULT13	TADULT14	TADULT15	TADULT16	TADULT17	TADULT18	TADULT19	TADULT20	TADULT21	TADULT22	TADULT23	TADULT24	TADULT25	TADULT26	TADULT27	TADULT28	TADULT29	TADULT30	TADULT31	TADULT32	TADULT33	TADULT34	TADULT35	TADULT36	TADULT37	TADULT38	TADULT39	TADULT40	TADULT41	TADULT42	TADULT43	TADULT44	TADULT45	TADULT46	TADULT47	TADULT48	TADULT49	TADULT50	TADULT51	TADULT52	TADULT53	TADULT54	TADULT55	TADULT56	TADULT57	TADULT58	TADULT59	TADULT60	TADULT61	TADULT62	TADULT63	TADULT64	TADULT65	TADULT66	TADULT67	TADULT68	TADULTBASE	THH01	THH02	THH03	THH04	THH05	THH06	THH07	THH08	THH09	THH11	THH12	THH13	THH14	THH15	THH16	THH17	THH18	THH19	THH20	THH21	THH22	THH23	THH24	THH25	THH26	THH27	THH28	THH29	THH30	THH31	THH32	THH33	THH34	THH35	THH36	THH37	THH38	THH39	THH40	THH41	THH42	THH43	THH44	THH45	THH46	THH47	THH48	THH49	THH50	THH51	THH52	THH53	THH54	THH55	THH56	THH57	THH58	THH59	THH60	THH61	THH62	THH63	THH64	THH65	THH66	THH67	THH68	THHBASE	THHGRPL1	THHGRPL11	THHGRPL12	THHGRPL13	THHGRPL14	THHGRPL2	THHGRPL3	THHGRPL4	THHGRPL5	THHGRPL6	THHGRPL7	THHGRPL8	THHGRPL9	THHGRPU1	THHGRPU2	THHGRPU3	THHGRPU4	THHGRPU5	THHGRPU6	TLIFECODE	TLIFENAME	TOTHH_CY	TOTHU_CY	TOTPOP_CY	TSEGCODE	TSEGNAME	TSEGNUM	UNEMPRT_CY	UNEMP_CY	VACANT_CY	VAL0_CY	VAL150K_CY	VAL1M_CY	VAL1PT5MCY	VAL250K_CY	VAL2M_CY	VAL50K_CY	VAL750K_CY	VALBASE_CY	WAGEBASECY	WHITE_CY	WHTFBASECY	WHTMBASECY	WIDOWED_CY
0	CA	7353	5808539	984770070408	1341852609490	1363545956974	10953288740347	186300	188412	374712	158042.920912	5808539	3057816	2750723	2056384	73844	3.50	2.92	100620	821339	637422	8055682	5552919	2345048	2345048	1186331	1158717	19595308	26629106	18678853	9166028	32063051	1246195	1267704	15535129	1399534	14709444	1533458	1442776	1332317	1220853	1252603	1262625	1290328	1247274	1173091	1008278	746364	527232	362402	447803	20024285	621048	1008060	39806791	8011720	9289070	813593	3342632	15757962	38993198	1280615	1084396	1102410	1036596	1486913	2149782	1630222	13335897	214901	94837	149373	953706	7017528	17243	15757962	7310374	4804568	7811593	7946369	374712	06	155779.2198	15076422	14203971	1298461	1348980	1481021	1618901	1503701	1378564	1227529	1251002	1245261	1228175	1301504	1076510	891235	637267	427533	272897	270401	19782506	32126345	15627854	36.2	56020	37.3	51.5	69051	35.1	99297	505800	10636136	25228888	1076167	1053726	California	11926897	159811	5713702	2195675	1176187	84542	141009	14577903	2557052	24048829	7102070	5	2806123	3484353	3617717	7102070	7294468	158252	158252	34254	79878	78374	2544656	2616684	30611551	2880555	28913415	3152359	2946477	2710881	2448382	2503605	2507886	2518503	2548778	2249601	1899513	1383631	954765	635299	718204	255.5	2129893	39806791	6041636	{'rings': [[[-13038833.5744, 3845968.519900001...	5627462	2067041	California	CA	6.458207e+11	5.955195e+06	962752	447008	877339	707623	1030736	998289	1614673	1324877	854053	428180	1051926	640890	507501	147970	237918	290669	232487	111099	175840	175248	27484	508845	34216	94745	26920	657657	2654588	898660	530885	587455	321133	1177243	208733	337293	394628	476335	299210	59480	140493	366232	156521	289040	189701	202710	74475	54655	10628	210357	24920	79246	244837	409205	166215	21790	118065	25748	113377	9029	1432303	1875298	534518	504305	79548	103401	172871	214308	121327	30611551	435771	183824	367891	297569	476909	507339	673663	508597	428604	259020	577307	263847	213335	66627	109074	143993	108873	52243	85167	78061	12137	244469	15552	49244	13249	260783	911215	362738	195394	210403	128040	543776	111747	169541	165386	226491	161769	28909	76061	193740	98928	146079	107181	112822	31553	22139	5084	95731	9515	37825	135236	200618	89406	11253	50951	12199	59308	4548	605661	641139	209939	214632	38327	25756	76824	50008	179	13336104	1761964	474338	127006	1709698	152588	2118203	1161232	543809	499350	412712	2068573	1407619	734811	1827341	4374461	1267838	4807134	591397	467754	2	Upscale Avenues	13336104	14383561	39806791	7B	Urban Villages	28	4.7	916455	1047457	190476	277000	678744	195039	432916	242837	160723	903689	7293065	21888277	21888277	10953440	10934837	1601462
1	TX	5115	1428081	663797428676	846781778790	860166554925	6544101733836	99642	101050	200692	264622.431161	1428081	736438	691643	1352089	65007	3.36	2.78	82927	640876	234140	5610338	3595450	3554094	3554094	1837649	1716445	13994294	18710254	13323060	7102474	23868426	1012163	958641	11007087	1023650	10392959	1088542	1028122	990111	912087	908014	888197	906409	1016393	815633	699159	508500	349890	230272	255883	14596063	796406	820030	28954616	5795902	7332348	603396	1902087	11500677	28351220	1099136	601595	972153	998707	1339610	1837748	1220161	10211181	108000	21467	126710	446880	3229534	4993	11500677	7563093	3900291	5712914	5787763	200692	48	261231.7115	10619025	9978508	1049926	1010959	1068334	1146033	1057803	1000356	900336	893942	864949	855845	1054854	749300	623922	440199	284583	169339	142828	14358553	22771838	11608584	34.8	48504	35.8	49.5	57286	33.8	89454	173734	7624216	16954978	459123	454026	Texas	7616564	92692	1406614	3427384	466269	37515	23827	11999638	1572787	17453939	3267049	44	1771782	1571022	1696027	3267049	6286745	28820	28820	29707	14439	14381	2062089	1969600	21626112	2091984	20371467	2234575	2085925	1990467	1812423	1801956	1753146	1762254	2071247	1564933	1323081	948699	634473	399611	398711	110.8	913149	28954616	3924542	{'rings': [[[-10822386.4159, 2999262.775600001...	4037002	1554142	Texas	TX	9.460813e+11	9.621068e+06	257841	546026	1102103	504579	298473	141680	25966	3941	232406	435495	27305	751696	718565	539687	401342	297639	111651	342816	282798	522498	225754	193715	190129	160088	240532	1662778	199271	827432	1492651	7383	1369349	27222	182557	574840	1656	145357	286509	195161	82795	95111	17082	114932	97451	70012	854973	411283	295295	344420	112278	4120	700747	410891	133118	94734	161445	243184	227922	183344	38365	84691	621868	44070	24312	72359	201481	199469	60997	21626112	124524	236631	510390	229203	148579	78268	11804	1715	123382	270425	15874	334179	322524	256069	194257	156003	54214	166876	141026	242077	106842	94754	92548	81196	118859	746527	76855	359005	603599	3263	582375	10984	106059	311328	831	73537	158365	91562	43855	55012	10078	56954	56138	36794	392512	198958	135159	153282	47834	2198	438819	218771	73760	51764	73393	116283	113907	88717	18279	32041	289865	21319	12423	18523	95654	64280	157	10211287	1249327	785312	392300	373927	178457	215169	344348	912772	712376	736276	2371624	752666	258831	670984	2543938	1519018	3382976	702334	1391880	7	Ethnic Enclaves	10211287	11236543	28954616	7A	Up and Coming Families	27	4.8	671234	1025256	561071	985102	66551	15750	527935	20493	1095680	94609	6285595	19562731	19562731	9877750	9684981	1165365
2	NY	5005	1774868	494056143933	721252426439	733561461654	5780147040071	57206	55978	113184	48359.759399	1774868	920657	854211	1234468	65662	3.27	2.59	95857	768205	451725	4524627	2844992	3202872	3202872	1720637	1482235	10297331	13980509	9750809	4711535	15422531	541010	622797	8316966	692667	7896904	736087	711718	665428	617325	653985	691792	725143	555213	671105	583941	447188	321376	227864	297383	10347030	556396	439969	20070143	4071725	4262278	575494	2207562	3920105	19494649	910026	562411	659740	628211	822195	1168671	908502	7524226	58228	16280	331729	299313	1562083	3773	3920105	1648699	3075758	1994498	1925607	113184	36	47126.3986	7603630	7176760	563187	645491	694720	735814	699391	649639	591431	624744	651555	664052	578913	595536	496269	361798	245015	158479	156451	9723113	16636184	7732110	39.0	51058	40.5	53.2	63751	37.6	87167	322649	5071787	9104863	369440	340441	New York	6469341	54956	1758588	2871143	410568	83316	6187	10965280	866788	16150038	1645399	33	1699757	825459	819940	1645399	3928983	9960	9960	36550	5029	4931	1104197	1268288	15920596	1387387	15073664	1471901	1411109	1315067	1208756	1278729	1343347	1389195	1134126	1266641	1080210	808986	566391	386343	453834	425.9	709881	20070143	3595416	{'rings': [[[-8259979.4246, 4949745.592100002]...	2182364	1012181	New York	NY	2.323782e+11	5.026351e+06	512090	105026	4623	566085	222285	250800	1375859	372083	36331	199329	565803	94595	113012	39543	373383	232188	170661	324706	344500	271067	329966	269307	19946	113275	239738	665	189396	20342	1016	1005	2888	696353	107184	38266	1333128	73019	173289	194976	25993	345965	23485	24115	145463	144351	204688	104636	1156	28446	11960	1150953	23619	17471	149676	107735	83723	278978	126444	50178	647696	52872	63074	73315	1056586	17171	106449	111054	39680	15920596	227930	45156	1971	245731	101807	129891	578041	153655	19294	114087	297684	42464	49670	19702	178348	115820	85493	169528	177703	127239	155409	132719	9562	57642	120941	284	63030	8326	352	535	1156	332041	60446	20774	569263	37915	95602	102941	13893	191240	11264	12914	83122	86221	98353	50421	584	14648	1916	521013	13514	9730	84095	58870	42581	150061	70282	28208	261620	17305	24745	34054	494486	4732	41867	10397	263	7524399	622595	687222	291132	832210	56996	880881	853619	111836	726892	603512	73683	1218982	398654	2497180	1108306	940871	2104912	239022	633845	8	Middle Ground	7524399	8391638	20070143	2B	Pleasantville	7	5.3	546522	867239	152062	379625	179685	56246	246121	99717	374729	263656	3926227	12613979	12613979	6448602	6165377	977829
3	FL	4133	585936	487005072197	613723856820	624450149363	6548612367402	40665	42463	83128	56491.338012	585936	316402	269534	1468305	59737	3.03	2.51	75281	803267	282862	5125816	2763015	3423250	3423250	1775199	1648051	9939670	14984858	9428446	5273287	15994410	540535	575794	8672285	647413	8288558	693040	652863	622949	602161	654907	696088	756439	556332	740359	711079	574407	426317	297361	348670	10667264	630616	434241	20875686	4066365	4142104	433652	1591194	5399203	20442034	951439	366646	868205	873200	1196903	1521148	970483	8152474	30738	13113	201778	241103	848135	3477	5399203	4060859	3663685	2732325	2666878	83128	12	53624.7587	8132434	7737236	561572	604324	667070	728403	671610	629910	591323	640640	666976	693978	574960	651105	617390	497457	358273	236773	224380	10208422	17479459	8404539	42.3	43866	43.9	55.4	52098	40.6	98640	212954	4819710	9694483	320999	306853	Florida	5579880	52390	572823	3221472	386749	50104	11742	11181203	770061	15476483	898239	10	2287450	433750	464489	898239	5193134	15219	15219	29913	7485	7734	1102107	1180118	16804719	1314483	16025794	1421443	1324473	1252859	1193484	1295547	1363064	1450417	1131292	1391464	1328469	1071864	784590	534134	573050	389.3	627852	20875686	2959407	{'rings': [[[-9124037.3451, 2817150.9723999985...	2990780	1107202	Florida	FL	1.866243e+11	9.761724e+06	139564	203442	252298	342663	283344	129690	129485	3407	78849	131796	30196	479436	544452	543860	408053	288055	142999	417240	573770	261579	85398	333616	2029	115622	143470	540715	227391	933973	104898	70517	460679	56767	240719	452235	0	207742	413860	143688	644510	279740	779970	766863	377196	100898	428649	183680	8535	297341	166979	48101	323008	451913	233004	178840	285711	217009	234897	250408	90659	40210	127174	79439	13767	21497	103498	109161	20707	16804719	64080	84617	112750	150048	139641	68081	53419	1152	40455	79640	18059	206336	237762	248795	195362	149440	70108	204783	289645	118846	39620	162626	1011	60130	70532	238120	80416	393622	42245	27259	185836	27665	135609	234795	0	103443	231898	69296	347422	155361	465971	402057	222582	59214	190835	81384	3488	138256	62390	20779	187750	229070	131273	90284	117945	108972	124281	117164	41705	15882	54240	35494	7223	4930	49689	35423	57	8152541	551136	659156	468362	154544	90042	163107	111977	692893	909338	452765	967498	802706	1652607	229713	1692280	1574873	2924108	1011180	720330	9	Senior Styles	8152541	9790195	20875686	9C	The Elders	42	5.1	511224	1637654	351168	819037	78889	20495	557404	36949	600047	111697	5192714	15242062	15242062	7772764	7469298	1199573
4	PA	3210	462360	315983343213	411902114464	418518490501	4125980999869	16036	16102	32138	45285.896438	462360	240313	222047	749637	61749	3.02	2.46	80493	806294	236846	3167219	1763503	1470953	1470953	765013	705940	6619516	9125523	6217293	3282447	9921694	335276	404682	5362136	433612	5076349	410643	397480	389122	375532	411895	447624	487329	355242	461925	408531	312098	231110	173218	235227	6642616	345422	268314	12992598	2573675	2724068	426311	1120165	987581	12566287	549498	299343	505620	485138	661013	933563	649666	5117219	13841	4066	72801	81708	393883	1223	987581	420059	2817515	484730	502851	32138	42	44742.7032	5010352	4726214	349790	417152	441081	428491	405440	392100	371081	405311	435283	463856	369830	429073	366389	265594	180573	120018	120580	6349982	10822050	5289824	41.4	46696	43.0	54.9	57362	39.8	126527	185452	2988934	3115252	161589	152711	Pennsylvania	3726095	18297	458294	1398152	232592	16338	3998	9877346	286064	12005017	410221	39	1270388	195431	214790	410221	3463374	5221	5221	32212	2622	2599	685066	821834	10372488	874693	9802563	839134	802920	781222	746613	817206	882907	951185	725072	890998	774920	577692	411683	293236	355807	290.4	314300	12992598	1653953	{'rings': [[[-8382772.6493, 4844948.8923999965...	1457813	585404	Pennsylvania	PA	2.053022e+11	2.068588e+06	162479	224406	22707	432943	210046	93639	202612	1754	84748	134652	24943	212966	62172	134424	548302	271213	517207	239410	553292	575890	903871	107575	38382	88283	504795	7672	14931	45556	6099	2557	6112	108325	136072	71952	14130	273695	196055	165969	5495	221381	17439	19227	190704	111879	144975	257849	36862	11142	18502	63122	23301	34327	146156	125056	113432	220217	317061	254390	17469	0	2886	271578	15924	0	143081	121146	29470	10372488	74277	98686	10729	196059	101319	45957	93730	645	45424	82738	12942	99476	28229	66028	267265	135979	258794	127103	278212	269579	435594	52290	18313	44342	263868	3585	6189	20132	2629	902	2884	53850	73109	39839	6765	137745	110110	84780	2896	116222	10022	10151	101801	66453	70968	124551	18223	5868	6096	31133	14285	17713	80722	66212	56799	116780	175679	129971	7816	0	1255	123001	7629	0	57457	25114	82	5117327	481070	210065	479229	139701	82571	185756	118011	193733	1067353	1083986	36321	506198	307545	287794	465570	1070547	1730882	522496	1039956	6	Cozy Country Living	5117327	5703807	12992598	6B	Salt of the Earth	22	6.1	402223	586480	222750	599167	27840	4755	326829	6624	516090	45734	3463173	10297405	10297405	5261612	5035793	725053

	Provider_Count	AAGEBASECY	AGGDI_CY	AGGHINC_CY	AGGINC_CY	AGGNW_CY	AIFBASE_CY	AIMBASE_CY	AMERIND_CY	AREA	ASIAN_CY	ASNFBASECY	ASNMBASECY	ASSCDEG_CY	AVGDI_CY	AVGFMSZ_CY	AVGHHSZ_CY	AVGHINC_CY	AVGNW_CY	AVGVAL_CY	BABYBOOMCY	BACHDEG_CY	BAGEBASECY	BLACK_CY	BLKFBASECY	BLKMBASECY	CIVLBFR_CY	EDUCBASECY	EMP_CY	FAMHH_CY	FAMPOP_CY	FEM0_CY	FEM15_CY	FEM18UP_CY	FEM20_CY	FEM21UP_CY	FEM25_CY	FEM30_CY	FEM35_CY	FEM40_CY	FEM45_CY	FEM50_CY	FEM55_CY	FEM5_CY	FEM60_CY	FEM65_CY	FEM70_CY	FEM75_CY	FEM80_CY	FEM85_CY	FEMALES_CY	GED_CY	GENALPHACY	GENBASE_CY	GENX_CY	GENZ_CY	GQPOP_CY	GRADDEG_CY	HAGEBASECY	HHPOP_CY	HINC0_CY	HINC150_CY	HINC15_CY	HINC25_CY	HINC35_CY	HINC50_CY	HINC75_CY	HINCBASECY	HISPAI_CY	HISPASN_CY	HISPBLK_CY	HISPMLT_CY	HISPOTH_CY	HISPPI_CY	HISPPOP_CY	HISPWHT_CY	HSGRAD_CY	HSPFBASECY	HSPMBASECY	IAGEBASECY	LANDAREA	MAL18UP_CY	MAL21UP_CY	MALE0_CY	MALE15_CY	MALE20_CY	MALE25_CY	MALE30_CY	MALE35_CY	MALE40_CY	MALE45_CY	MALE50_CY	MALE55_CY	MALE5_CY	MALE60_CY	MALE65_CY	MALE70_CY	MALE75_CY	MALE80_CY	MALE85_CY	MALES_CY	MARBASE_CY	MARRIED_CY	MEDAGE_CY	MEDDI_CY	MEDFAGE_CY	MEDHHR_CY	MEDHINC_CY	MEDMAGE_CY	MEDNW_CY	MEDVAL_CY	MILLENN_CY	MINORITYCY	MLTFBASECY	MLTMBASECY	NEVMARR_CY	NHSPAI_CY	NHSPASN_CY	NHSPBLK_CY	NHSPMLT_CY	NHSPOTH_CY	NHSPPI_CY	NHSPWHT_CY	NOHS_CY	NONHISP_CY	OAGEBASECY	OLDRGENSCY	OTHFBASECY	OTHMBASECY	OTHRACE_CY	OWNER_CY	PACIFIC_CY	PAGEBASECY	PCI_CY	PIFBASE_CY	PIMBASE_CY	POP0_CY	POP15_CY	POP18UP_CY	POP20_CY	POP21UP_CY	POP25_CY	POP30_CY	POP35_CY	POP40_CY	POP45_CY	POP50_CY	POP55_CY	POP5_CY	POP60_CY	POP65_CY	POP70_CY	POP75_CY	POP80_CY	POP85_CY	POPDENS_CY	RACE2UP_CY	RACEBASECY	RENTER_CY	SMCOLL_CY	SOMEHS_CY	TADULT01	TADULT02	TADULT03	TADULT04	TADULT05	TADULT06	TADULT07	TADULT08	TADULT09	TADULT11	TADULT12	TADULT13	TADULT14	TADULT15	TADULT16	TADULT17	TADULT18	TADULT19	TADULT20	TADULT21	TADULT22	TADULT23	TADULT24	TADULT25	TADULT26	TADULT27	TADULT28	TADULT29	TADULT30	TADULT31	TADULT32	TADULT33	TADULT34	TADULT35	TADULT36	TADULT37	TADULT38	TADULT39	TADULT40	TADULT41	TADULT42	TADULT43	TADULT44	TADULT45	TADULT46	TADULT47	TADULT48	TADULT49	TADULT50	TADULT51	TADULT52	TADULT53	TADULT54	TADULT55	TADULT56	TADULT57	TADULT58	TADULT59	TADULT60	TADULT61	TADULT62	TADULT63	TADULT64	TADULT65	TADULT66	TADULT67	TADULT68	TADULTBASE	THH01	THH02	THH03	THH04	THH05	THH06	THH07	THH08	THH09	THH11	THH12	THH13	THH14	THH15	THH16	THH17	THH18	THH19	THH20	THH21	THH22	THH23	THH24	THH25	THH26	THH27	THH28	THH29	THH30	THH31	THH32	THH33	THH34	THH35	THH36	THH37	THH38	THH39	THH40	THH41	THH42	THH43	THH44	THH45	THH46	THH47	THH48	THH49	THH50	THH51	THH52	THH53	THH54	THH55	THH56	THH57	THH58	THH59	THH60	THH61	THH62	THH63	THH64	THH65	THH66	THH67	THH68	THHBASE	THHGRPL1	THHGRPL11	THHGRPL12	THHGRPL13	THHGRPL14	THHGRPL2	THHGRPL3	THHGRPL4	THHGRPL5	THHGRPL6	THHGRPL7	THHGRPL8	THHGRPL9	THHGRPU1	THHGRPU2	THHGRPU3	THHGRPU4	THHGRPU5	THHGRPU6	TOTHH_CY	TOTHU_CY	TOTPOP_CY	TSEGNUM	UNEMPRT_CY	UNEMP_CY	VACANT_CY	VAL0_CY	VAL150K_CY	VAL1M_CY	VAL1PT5MCY	VAL250K_CY	VAL2M_CY	VAL50K_CY	VAL750K_CY	VALBASE_CY	WAGEBASECY	WHITE_CY	WHTFBASECY	WHTMBASECY	WIDOWED_CY
0	7353	5808539	984770070408	1341852609490	1363545956974	10953288740347	186300	188412	374712	158042.920912	5808539	3057816	2750723	2056384	73844	3.50	2.92	100620	821339	637422	8055682	5552919	2345048	2345048	1186331	1158717	19595308	26629106	18678853	9166028	32063051	1246195	1267704	15535129	1399534	14709444	1533458	1442776	1332317	1220853	1252603	1262625	1290328	1247274	1173091	1008278	746364	527232	362402	447803	20024285	621048	1008060	39806791	8011720	9289070	813593	3342632	15757962	38993198	1280615	1084396	1102410	1036596	1486913	2149782	1630222	13335897	214901	94837	149373	953706	7017528	17243	15757962	7310374	4804568	7811593	7946369	374712	155779.2198	15076422	14203971	1298461	1348980	1481021	1618901	1503701	1378564	1227529	1251002	1245261	1228175	1301504	1076510	891235	637267	427533	272897	270401	19782506	32126345	15627854	36.2	56020	37.3	51.5	69051	35.1	99297	505800	10636136	25228888	1076167	1053726	11926897	159811	5713702	2195675	1176187	84542	141009	14577903	2557052	24048829	7102070	2806123	3484353	3617717	7102070	7294468	158252	158252	34254	79878	78374	2544656	2616684	30611551	2880555	28913415	3152359	2946477	2710881	2448382	2503605	2507886	2518503	2548778	2249601	1899513	1383631	954765	635299	718204	255.5	2129893	39806791	6041636	5627462	2067041	962752	447008	877339	707623	1030736	998289	1614673	1324877	854053	428180	1051926	640890	507501	147970	237918	290669	232487	111099	175840	175248	27484	508845	34216	94745	26920	657657	2654588	898660	530885	587455	321133	1177243	208733	337293	394628	476335	299210	59480	140493	366232	156521	289040	189701	202710	74475	54655	10628	210357	24920	79246	244837	409205	166215	21790	118065	25748	113377	9029	1432303	1875298	534518	504305	79548	103401	172871	214308	121327	30611551	435771	183824	367891	297569	476909	507339	673663	508597	428604	259020	577307	263847	213335	66627	109074	143993	108873	52243	85167	78061	12137	244469	15552	49244	13249	260783	911215	362738	195394	210403	128040	543776	111747	169541	165386	226491	161769	28909	76061	193740	98928	146079	107181	112822	31553	22139	5084	95731	9515	37825	135236	200618	89406	11253	50951	12199	59308	4548	605661	641139	209939	214632	38327	25756	76824	50008	179	13336104	1761964	474338	127006	1709698	152588	2118203	1161232	543809	499350	412712	2068573	1407619	734811	1827341	4374461	1267838	4807134	591397	467754	13336104	14383561	39806791	28	4.7	916455	1047457	190476	277000	678744	195039	432916	242837	160723	903689	7293065	21888277	21888277	10953440	10934837	1601462
1	5115	1428081	663797428676	846781778790	860166554925	6544101733836	99642	101050	200692	264622.431161	1428081	736438	691643	1352089	65007	3.36	2.78	82927	640876	234140	5610338	3595450	3554094	3554094	1837649	1716445	13994294	18710254	13323060	7102474	23868426	1012163	958641	11007087	1023650	10392959	1088542	1028122	990111	912087	908014	888197	906409	1016393	815633	699159	508500	349890	230272	255883	14596063	796406	820030	28954616	5795902	7332348	603396	1902087	11500677	28351220	1099136	601595	972153	998707	1339610	1837748	1220161	10211181	108000	21467	126710	446880	3229534	4993	11500677	7563093	3900291	5712914	5787763	200692	261231.7115	10619025	9978508	1049926	1010959	1068334	1146033	1057803	1000356	900336	893942	864949	855845	1054854	749300	623922	440199	284583	169339	142828	14358553	22771838	11608584	34.8	48504	35.8	49.5	57286	33.8	89454	173734	7624216	16954978	459123	454026	7616564	92692	1406614	3427384	466269	37515	23827	11999638	1572787	17453939	3267049	1771782	1571022	1696027	3267049	6286745	28820	28820	29707	14439	14381	2062089	1969600	21626112	2091984	20371467	2234575	2085925	1990467	1812423	1801956	1753146	1762254	2071247	1564933	1323081	948699	634473	399611	398711	110.8	913149	28954616	3924542	4037002	1554142	257841	546026	1102103	504579	298473	141680	25966	3941	232406	435495	27305	751696	718565	539687	401342	297639	111651	342816	282798	522498	225754	193715	190129	160088	240532	1662778	199271	827432	1492651	7383	1369349	27222	182557	574840	1656	145357	286509	195161	82795	95111	17082	114932	97451	70012	854973	411283	295295	344420	112278	4120	700747	410891	133118	94734	161445	243184	227922	183344	38365	84691	621868	44070	24312	72359	201481	199469	60997	21626112	124524	236631	510390	229203	148579	78268	11804	1715	123382	270425	15874	334179	322524	256069	194257	156003	54214	166876	141026	242077	106842	94754	92548	81196	118859	746527	76855	359005	603599	3263	582375	10984	106059	311328	831	73537	158365	91562	43855	55012	10078	56954	56138	36794	392512	198958	135159	153282	47834	2198	438819	218771	73760	51764	73393	116283	113907	88717	18279	32041	289865	21319	12423	18523	95654	64280	157	10211287	1249327	785312	392300	373927	178457	215169	344348	912772	712376	736276	2371624	752666	258831	670984	2543938	1519018	3382976	702334	1391880	10211287	11236543	28954616	27	4.8	671234	1025256	561071	985102	66551	15750	527935	20493	1095680	94609	6285595	19562731	19562731	9877750	9684981	1165365
2	5005	1774868	494056143933	721252426439	733561461654	5780147040071	57206	55978	113184	48359.759399	1774868	920657	854211	1234468	65662	3.27	2.59	95857	768205	451725	4524627	2844992	3202872	3202872	1720637	1482235	10297331	13980509	9750809	4711535	15422531	541010	622797	8316966	692667	7896904	736087	711718	665428	617325	653985	691792	725143	555213	671105	583941	447188	321376	227864	297383	10347030	556396	439969	20070143	4071725	4262278	575494	2207562	3920105	19494649	910026	562411	659740	628211	822195	1168671	908502	7524226	58228	16280	331729	299313	1562083	3773	3920105	1648699	3075758	1994498	1925607	113184	47126.3986	7603630	7176760	563187	645491	694720	735814	699391	649639	591431	624744	651555	664052	578913	595536	496269	361798	245015	158479	156451	9723113	16636184	7732110	39.0	51058	40.5	53.2	63751	37.6	87167	322649	5071787	9104863	369440	340441	6469341	54956	1758588	2871143	410568	83316	6187	10965280	866788	16150038	1645399	1699757	825459	819940	1645399	3928983	9960	9960	36550	5029	4931	1104197	1268288	15920596	1387387	15073664	1471901	1411109	1315067	1208756	1278729	1343347	1389195	1134126	1266641	1080210	808986	566391	386343	453834	425.9	709881	20070143	3595416	2182364	1012181	512090	105026	4623	566085	222285	250800	1375859	372083	36331	199329	565803	94595	113012	39543	373383	232188	170661	324706	344500	271067	329966	269307	19946	113275	239738	665	189396	20342	1016	1005	2888	696353	107184	38266	1333128	73019	173289	194976	25993	345965	23485	24115	145463	144351	204688	104636	1156	28446	11960	1150953	23619	17471	149676	107735	83723	278978	126444	50178	647696	52872	63074	73315	1056586	17171	106449	111054	39680	15920596	227930	45156	1971	245731	101807	129891	578041	153655	19294	114087	297684	42464	49670	19702	178348	115820	85493	169528	177703	127239	155409	132719	9562	57642	120941	284	63030	8326	352	535	1156	332041	60446	20774	569263	37915	95602	102941	13893	191240	11264	12914	83122	86221	98353	50421	584	14648	1916	521013	13514	9730	84095	58870	42581	150061	70282	28208	261620	17305	24745	34054	494486	4732	41867	10397	263	7524399	622595	687222	291132	832210	56996	880881	853619	111836	726892	603512	73683	1218982	398654	2497180	1108306	940871	2104912	239022	633845	7524399	8391638	20070143	7	5.3	546522	867239	152062	379625	179685	56246	246121	99717	374729	263656	3926227	12613979	12613979	6448602	6165377	977829
3	4133	585936	487005072197	613723856820	624450149363	6548612367402	40665	42463	83128	56491.338012	585936	316402	269534	1468305	59737	3.03	2.51	75281	803267	282862	5125816	2763015	3423250	3423250	1775199	1648051	9939670	14984858	9428446	5273287	15994410	540535	575794	8672285	647413	8288558	693040	652863	622949	602161	654907	696088	756439	556332	740359	711079	574407	426317	297361	348670	10667264	630616	434241	20875686	4066365	4142104	433652	1591194	5399203	20442034	951439	366646	868205	873200	1196903	1521148	970483	8152474	30738	13113	201778	241103	848135	3477	5399203	4060859	3663685	2732325	2666878	83128	53624.7587	8132434	7737236	561572	604324	667070	728403	671610	629910	591323	640640	666976	693978	574960	651105	617390	497457	358273	236773	224380	10208422	17479459	8404539	42.3	43866	43.9	55.4	52098	40.6	98640	212954	4819710	9694483	320999	306853	5579880	52390	572823	3221472	386749	50104	11742	11181203	770061	15476483	898239	2287450	433750	464489	898239	5193134	15219	15219	29913	7485	7734	1102107	1180118	16804719	1314483	16025794	1421443	1324473	1252859	1193484	1295547	1363064	1450417	1131292	1391464	1328469	1071864	784590	534134	573050	389.3	627852	20875686	2959407	2990780	1107202	139564	203442	252298	342663	283344	129690	129485	3407	78849	131796	30196	479436	544452	543860	408053	288055	142999	417240	573770	261579	85398	333616	2029	115622	143470	540715	227391	933973	104898	70517	460679	56767	240719	452235	0	207742	413860	143688	644510	279740	779970	766863	377196	100898	428649	183680	8535	297341	166979	48101	323008	451913	233004	178840	285711	217009	234897	250408	90659	40210	127174	79439	13767	21497	103498	109161	20707	16804719	64080	84617	112750	150048	139641	68081	53419	1152	40455	79640	18059	206336	237762	248795	195362	149440	70108	204783	289645	118846	39620	162626	1011	60130	70532	238120	80416	393622	42245	27259	185836	27665	135609	234795	0	103443	231898	69296	347422	155361	465971	402057	222582	59214	190835	81384	3488	138256	62390	20779	187750	229070	131273	90284	117945	108972	124281	117164	41705	15882	54240	35494	7223	4930	49689	35423	57	8152541	551136	659156	468362	154544	90042	163107	111977	692893	909338	452765	967498	802706	1652607	229713	1692280	1574873	2924108	1011180	720330	8152541	9790195	20875686	42	5.1	511224	1637654	351168	819037	78889	20495	557404	36949	600047	111697	5192714	15242062	15242062	7772764	7469298	1199573
4	3210	462360	315983343213	411902114464	418518490501	4125980999869	16036	16102	32138	45285.896438	462360	240313	222047	749637	61749	3.02	2.46	80493	806294	236846	3167219	1763503	1470953	1470953	765013	705940	6619516	9125523	6217293	3282447	9921694	335276	404682	5362136	433612	5076349	410643	397480	389122	375532	411895	447624	487329	355242	461925	408531	312098	231110	173218	235227	6642616	345422	268314	12992598	2573675	2724068	426311	1120165	987581	12566287	549498	299343	505620	485138	661013	933563	649666	5117219	13841	4066	72801	81708	393883	1223	987581	420059	2817515	484730	502851	32138	44742.7032	5010352	4726214	349790	417152	441081	428491	405440	392100	371081	405311	435283	463856	369830	429073	366389	265594	180573	120018	120580	6349982	10822050	5289824	41.4	46696	43.0	54.9	57362	39.8	126527	185452	2988934	3115252	161589	152711	3726095	18297	458294	1398152	232592	16338	3998	9877346	286064	12005017	410221	1270388	195431	214790	410221	3463374	5221	5221	32212	2622	2599	685066	821834	10372488	874693	9802563	839134	802920	781222	746613	817206	882907	951185	725072	890998	774920	577692	411683	293236	355807	290.4	314300	12992598	1653953	1457813	585404	162479	224406	22707	432943	210046	93639	202612	1754	84748	134652	24943	212966	62172	134424	548302	271213	517207	239410	553292	575890	903871	107575	38382	88283	504795	7672	14931	45556	6099	2557	6112	108325	136072	71952	14130	273695	196055	165969	5495	221381	17439	19227	190704	111879	144975	257849	36862	11142	18502	63122	23301	34327	146156	125056	113432	220217	317061	254390	17469	0	2886	271578	15924	0	143081	121146	29470	10372488	74277	98686	10729	196059	101319	45957	93730	645	45424	82738	12942	99476	28229	66028	267265	135979	258794	127103	278212	269579	435594	52290	18313	44342	263868	3585	6189	20132	2629	902	2884	53850	73109	39839	6765	137745	110110	84780	2896	116222	10022	10151	101801	66453	70968	124551	18223	5868	6096	31133	14285	17713	80722	66212	56799	116780	175679	129971	7816	0	1255	123001	7629	0	57457	25114	82	5117327	481070	210065	479229	139701	82571	185756	118011	193733	1067353	1083986	36321	506198	307545	287794	465570	1070547	1730882	522496	1039956	5117327	5703807	12992598	22	6.1	402223	586480	222750	599167	27840	4755	326829	6624	516090	45734	3463173	10297405	10297405	5261612	5035793	725053

	regionabbr	subregion	Provider_Count	AAGEBASECY	AGGDI_CY	AGGHINC_CY	AGGINC_CY	AGGNW_CY	AIFBASE_CY	AIMBASE_CY	AMERIND_CY	AREA	ASIAN_CY	ASNFBASECY	ASNMBASECY	ASSCDEG_CY	AVGDI_CY	AVGFMSZ_CY	AVGHHSZ_CY	AVGHINC_CY	AVGNW_CY	AVGVAL_CY	BABYBOOMCY	BACHDEG_CY	BAGEBASECY	BLACK_CY	BLKFBASECY	BLKMBASECY	CIVLBFR_CY	EDUCBASECY	EMP_CY	FAMHH_CY	FAMPOP_CY	FEM0_CY	FEM15_CY	FEM18UP_CY	FEM20_CY	FEM21UP_CY	FEM25_CY	FEM30_CY	FEM35_CY	FEM40_CY	FEM45_CY	FEM50_CY	FEM55_CY	FEM5_CY	FEM60_CY	FEM65_CY	FEM70_CY	FEM75_CY	FEM80_CY	FEM85_CY	FEMALES_CY	GED_CY	GENALPHACY	GENBASE_CY	GENX_CY	GENZ_CY	GQPOP_CY	GRADDEG_CY	HAGEBASECY	HHPOP_CY	HINC0_CY	HINC150_CY	HINC15_CY	HINC25_CY	HINC35_CY	HINC50_CY	HINC75_CY	HINCBASECY	HISPAI_CY	HISPASN_CY	HISPBLK_CY	HISPMLT_CY	HISPOTH_CY	HISPPI_CY	HISPPOP_CY	HISPWHT_CY	HSGRAD_CY	HSPFBASECY	HSPMBASECY	IAGEBASECY	LANDAREA	MAL18UP_CY	MAL21UP_CY	MALE0_CY	MALE15_CY	MALE20_CY	MALE25_CY	MALE30_CY	MALE35_CY	MALE40_CY	MALE45_CY	MALE50_CY	MALE55_CY	MALE5_CY	MALE60_CY	MALE65_CY	MALE70_CY	MALE75_CY	MALE80_CY	MALE85_CY	MALES_CY	MARBASE_CY	MARRIED_CY	MEDAGE_CY	MEDDI_CY	MEDFAGE_CY	MEDHHR_CY	MEDHINC_CY	MEDMAGE_CY	MEDNW_CY	MEDVAL_CY	MILLENN_CY	MINORITYCY	MLTFBASECY	MLTMBASECY	NEVMARR_CY	NHSPAI_CY	NHSPASN_CY	NHSPBLK_CY	NHSPMLT_CY	NHSPOTH_CY	NHSPPI_CY	NHSPWHT_CY	NOHS_CY	NONHISP_CY	OAGEBASECY	OLDRGENSCY	OTHFBASECY	OTHMBASECY	OTHRACE_CY	OWNER_CY	PACIFIC_CY	PAGEBASECY	PCI_CY	PIFBASE_CY	PIMBASE_CY	POP0_CY	POP15_CY	POP18UP_CY	POP20_CY	POP21UP_CY	POP25_CY	POP30_CY	POP35_CY	POP40_CY	POP45_CY	POP50_CY	POP55_CY	POP5_CY	POP60_CY	POP65_CY	POP70_CY	POP75_CY	POP80_CY	POP85_CY	POPDENS_CY	RACE2UP_CY	RACEBASECY	RENTER_CY	SMCOLL_CY	SOMEHS_CY	TOTHH_CY	TOTHU_CY	TOTPOP_CY	UNEMPRT_CY	UNEMP_CY	VACANT_CY	WAGEBASECY	WHITE_CY	WHTFBASECY	WHTMBASECY	WIDOWED_CY
29	CA	City and County of San Francisco	330.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
30	CA	Sacramento	329.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
35	MD	City of Baltimore	298.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
39	HI	City and County of Honolulu	286.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
50	CO	City and County of Denver	251.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

	Provider_Count	AAGEBASECY	AGGDI_CY	AGGHINC_CY	AGGINC_CY	AGGNW_CY	AIFBASE_CY	AIMBASE_CY	AMERIND_CY	AREA	ASIAN_CY	ASNFBASECY	ASNMBASECY	ASSCDEG_CY	AVGDI_CY	AVGFMSZ_CY	AVGHHSZ_CY	AVGHINC_CY	AVGNW_CY	AVGVAL_CY	BABYBOOMCY	BACHDEG_CY	BAGEBASECY	BLACK_CY	BLKFBASECY	BLKMBASECY	CIVLBFR_CY	EDUCBASECY	EMP_CY	FAMHH_CY	FAMPOP_CY	FEM0_CY	FEM15_CY	FEM18UP_CY	FEM20_CY	FEM21UP_CY	FEM25_CY	FEM30_CY	FEM35_CY	FEM40_CY	FEM45_CY	FEM50_CY	FEM55_CY	FEM5_CY	FEM60_CY	FEM65_CY	FEM70_CY	FEM75_CY	FEM80_CY	FEM85_CY	FEMALES_CY	GED_CY	GENALPHACY	GENBASE_CY	GENX_CY	GENZ_CY	GQPOP_CY	GRADDEG_CY	HAGEBASECY	HHPOP_CY	HINC0_CY	HINC150_CY	HINC15_CY	HINC25_CY	HINC35_CY	HINC50_CY	HINC75_CY	HINCBASECY	HISPAI_CY	HISPASN_CY	HISPBLK_CY	HISPMLT_CY	HISPOTH_CY	HISPPI_CY	HISPPOP_CY	HISPWHT_CY	HSGRAD_CY	HSPFBASECY	HSPMBASECY	IAGEBASECY	LANDAREA	MAL18UP_CY	MAL21UP_CY	MALE0_CY	MALE15_CY	MALE20_CY	MALE25_CY	MALE30_CY	MALE35_CY	MALE40_CY	MALE45_CY	MALE50_CY	MALE55_CY	MALE5_CY	MALE60_CY	MALE65_CY	MALE70_CY	MALE75_CY	MALE80_CY	MALE85_CY	MALES_CY	MARBASE_CY	MARRIED_CY	MEDAGE_CY	MEDDI_CY	MEDFAGE_CY	MEDHHR_CY	MEDHINC_CY	MEDMAGE_CY	MEDNW_CY	MEDVAL_CY	MILLENN_CY	MINORITYCY	MLTFBASECY	MLTMBASECY	NEVMARR_CY	NHSPAI_CY	NHSPASN_CY	NHSPBLK_CY	NHSPMLT_CY	NHSPOTH_CY	NHSPPI_CY	NHSPWHT_CY	NOHS_CY	NONHISP_CY	OAGEBASECY	OLDRGENSCY	OTHFBASECY	OTHMBASECY	OTHRACE_CY	OWNER_CY	PACIFIC_CY	PAGEBASECY	PCI_CY	PIFBASE_CY	PIMBASE_CY	POP0_CY	POP15_CY	POP18UP_CY	POP20_CY	POP21UP_CY	POP25_CY	POP30_CY	POP35_CY	POP40_CY	POP45_CY	POP50_CY	POP55_CY	POP5_CY	POP60_CY	POP65_CY	POP70_CY	POP75_CY	POP80_CY	POP85_CY	POPDENS_CY	RACE2UP_CY	RACEBASECY	RENTER_CY	SMCOLL_CY	SOMEHS_CY	TADULT01	TADULT02	TADULT03	TADULT04	TADULT05	TADULT06	TADULT07	TADULT08	TADULT09	TADULT11	TADULT12	TADULT13	TADULT14	TADULT15	TADULT16	TADULT17	TADULT18	TADULT19	TADULT20	TADULT21	TADULT22	TADULT23	TADULT24	TADULT25	TADULT26	TADULT27	TADULT28	TADULT29	TADULT30	TADULT31	TADULT32	TADULT33	TADULT34	TADULT35	TADULT36	TADULT37	TADULT38	TADULT39	TADULT40	TADULT41	TADULT42	TADULT43	TADULT44	TADULT45	TADULT46	TADULT47	TADULT48	TADULT49	TADULT50	TADULT51	TADULT52	TADULT53	TADULT54	TADULT55	TADULT56	TADULT57	TADULT58	TADULT59	TADULT60	TADULT61	TADULT62	TADULT63	TADULT64	TADULT65	TADULT66	TADULT67	TADULT68	TADULTBASE	THH01	THH02	THH03	THH04	THH05	THH06	THH07	THH08	THH09	THH11	THH12	THH13	THH14	THH15	THH16	THH17	THH18	THH19	THH20	THH21	THH22	THH23	THH24	THH25	THH26	THH27	THH28	THH29	THH30	THH31	THH32	THH33	THH34	THH35	THH36	THH37	THH38	THH39	THH40	THH41	THH42	THH43	THH44	THH45	THH46	THH47	THH48	THH49	THH50	THH51	THH52	THH53	THH54	THH55	THH56	THH57	THH58	THH59	THH60	THH61	THH62	THH63	THH64	THH65	THH66	THH67	THH68	THHBASE	THHGRPL1	THHGRPL11	THHGRPL12	THHGRPL13	THHGRPL14	THHGRPL2	THHGRPL3	THHGRPL4	THHGRPL5	THHGRPL6	THHGRPL7	THHGRPL8	THHGRPL9	THHGRPU1	THHGRPU2	THHGRPU3	THHGRPU4	THHGRPU5	THHGRPU6	TOTHH_CY	TOTHU_CY	TOTPOP_CY	TSEGNUM	UNEMPRT_CY	UNEMP_CY	VACANT_CY	VAL0_CY	VAL150K_CY	VAL1M_CY	VAL1PT5MCY	VAL250K_CY	VAL2M_CY	VAL50K_CY	VAL750K_CY	VALBASE_CY	WAGEBASECY	WHITE_CY	WHTFBASECY	WHTMBASECY	WIDOWED_CY
0	7353.0	5808539	984770070408	1341852609490	1363545956974	10953288740347	186300	188412	374712	158042.920912	5808539	3057816	2750723	2056384	73844	3.50	2.92	100620	821339	637422	8055682	5552919	2345048	2345048	1186331	1158717	19595308	26629106	18678853	9166028	32063051	1246195	1267704	15535129	1399534	14709444	1533458	1442776	1332317	1220853	1252603	1262625	1290328	1247274	1173091	1008278	746364	527232	362402	447803	20024285	621048	1008060	39806791	8011720	9289070	813593	3342632	15757962	38993198	1280615	1084396	1102410	1036596	1486913	2149782	1630222	13335897	214901	94837	149373	953706	7017528	17243	15757962	7310374	4804568	7811593	7946369	374712	155779.2198	15076422	14203971	1298461	1348980	1481021	1618901	1503701	1378564	1227529	1251002	1245261	1228175	1301504	1076510	891235	637267	427533	272897	270401	19782506	32126345	15627854	36.2	56020	37.3	51.5	69051	35.1	99297	505800	10636136	25228888	1076167	1053726	11926897	159811	5713702	2195675	1176187	84542	141009	14577903	2557052	24048829	7102070	2806123	3484353	3617717	7102070	7294468	158252	158252	34254	79878	78374	2544656	2616684	30611551	2880555	28913415	3152359	2946477	2710881	2448382	2503605	2507886	2518503	2548778	2249601	1899513	1383631	954765	635299	718204	255.5	2129893	39806791	6041636	5627462	2067041	962752	447008	877339	707623	1030736	998289	1614673	1324877	854053	428180	1051926	640890	507501	147970	237918	290669	232487	111099	175840	175248	27484	508845	34216	94745	26920	657657	2654588	898660	530885	587455	321133	1177243	208733	337293	394628	476335	299210	59480	140493	366232	156521	289040	189701	202710	74475	54655	10628	210357	24920	79246	244837	409205	166215	21790	118065	25748	113377	9029	1432303	1875298	534518	504305	79548	103401	172871	214308	121327	30611551	435771	183824	367891	297569	476909	507339	673663	508597	428604	259020	577307	263847	213335	66627	109074	143993	108873	52243	85167	78061	12137	244469	15552	49244	13249	260783	911215	362738	195394	210403	128040	543776	111747	169541	165386	226491	161769	28909	76061	193740	98928	146079	107181	112822	31553	22139	5084	95731	9515	37825	135236	200618	89406	11253	50951	12199	59308	4548	605661	641139	209939	214632	38327	25756	76824	50008	179	13336104	1761964	474338	127006	1709698	152588	2118203	1161232	543809	499350	412712	2068573	1407619	734811	1827341	4374461	1267838	4807134	591397	467754	13336104	14383561	39806791	28	4.7	916455	1047457	190476	277000	678744	195039	432916	242837	160723	903689	7293065	21888277	21888277	10953440	10934837	1601462
1	5115.0	1428081	663797428676	846781778790	860166554925	6544101733836	99642	101050	200692	264622.431161	1428081	736438	691643	1352089	65007	3.36	2.78	82927	640876	234140	5610338	3595450	3554094	3554094	1837649	1716445	13994294	18710254	13323060	7102474	23868426	1012163	958641	11007087	1023650	10392959	1088542	1028122	990111	912087	908014	888197	906409	1016393	815633	699159	508500	349890	230272	255883	14596063	796406	820030	28954616	5795902	7332348	603396	1902087	11500677	28351220	1099136	601595	972153	998707	1339610	1837748	1220161	10211181	108000	21467	126710	446880	3229534	4993	11500677	7563093	3900291	5712914	5787763	200692	261231.7115	10619025	9978508	1049926	1010959	1068334	1146033	1057803	1000356	900336	893942	864949	855845	1054854	749300	623922	440199	284583	169339	142828	14358553	22771838	11608584	34.8	48504	35.8	49.5	57286	33.8	89454	173734	7624216	16954978	459123	454026	7616564	92692	1406614	3427384	466269	37515	23827	11999638	1572787	17453939	3267049	1771782	1571022	1696027	3267049	6286745	28820	28820	29707	14439	14381	2062089	1969600	21626112	2091984	20371467	2234575	2085925	1990467	1812423	1801956	1753146	1762254	2071247	1564933	1323081	948699	634473	399611	398711	110.8	913149	28954616	3924542	4037002	1554142	257841	546026	1102103	504579	298473	141680	25966	3941	232406	435495	27305	751696	718565	539687	401342	297639	111651	342816	282798	522498	225754	193715	190129	160088	240532	1662778	199271	827432	1492651	7383	1369349	27222	182557	574840	1656	145357	286509	195161	82795	95111	17082	114932	97451	70012	854973	411283	295295	344420	112278	4120	700747	410891	133118	94734	161445	243184	227922	183344	38365	84691	621868	44070	24312	72359	201481	199469	60997	21626112	124524	236631	510390	229203	148579	78268	11804	1715	123382	270425	15874	334179	322524	256069	194257	156003	54214	166876	141026	242077	106842	94754	92548	81196	118859	746527	76855	359005	603599	3263	582375	10984	106059	311328	831	73537	158365	91562	43855	55012	10078	56954	56138	36794	392512	198958	135159	153282	47834	2198	438819	218771	73760	51764	73393	116283	113907	88717	18279	32041	289865	21319	12423	18523	95654	64280	157	10211287	1249327	785312	392300	373927	178457	215169	344348	912772	712376	736276	2371624	752666	258831	670984	2543938	1519018	3382976	702334	1391880	10211287	11236543	28954616	27	4.8	671234	1025256	561071	985102	66551	15750	527935	20493	1095680	94609	6285595	19562731	19562731	9877750	9684981	1165365
2	5005.0	1774868	494056143933	721252426439	733561461654	5780147040071	57206	55978	113184	48359.759399	1774868	920657	854211	1234468	65662	3.27	2.59	95857	768205	451725	4524627	2844992	3202872	3202872	1720637	1482235	10297331	13980509	9750809	4711535	15422531	541010	622797	8316966	692667	7896904	736087	711718	665428	617325	653985	691792	725143	555213	671105	583941	447188	321376	227864	297383	10347030	556396	439969	20070143	4071725	4262278	575494	2207562	3920105	19494649	910026	562411	659740	628211	822195	1168671	908502	7524226	58228	16280	331729	299313	1562083	3773	3920105	1648699	3075758	1994498	1925607	113184	47126.3986	7603630	7176760	563187	645491	694720	735814	699391	649639	591431	624744	651555	664052	578913	595536	496269	361798	245015	158479	156451	9723113	16636184	7732110	39.0	51058	40.5	53.2	63751	37.6	87167	322649	5071787	9104863	369440	340441	6469341	54956	1758588	2871143	410568	83316	6187	10965280	866788	16150038	1645399	1699757	825459	819940	1645399	3928983	9960	9960	36550	5029	4931	1104197	1268288	15920596	1387387	15073664	1471901	1411109	1315067	1208756	1278729	1343347	1389195	1134126	1266641	1080210	808986	566391	386343	453834	425.9	709881	20070143	3595416	2182364	1012181	512090	105026	4623	566085	222285	250800	1375859	372083	36331	199329	565803	94595	113012	39543	373383	232188	170661	324706	344500	271067	329966	269307	19946	113275	239738	665	189396	20342	1016	1005	2888	696353	107184	38266	1333128	73019	173289	194976	25993	345965	23485	24115	145463	144351	204688	104636	1156	28446	11960	1150953	23619	17471	149676	107735	83723	278978	126444	50178	647696	52872	63074	73315	1056586	17171	106449	111054	39680	15920596	227930	45156	1971	245731	101807	129891	578041	153655	19294	114087	297684	42464	49670	19702	178348	115820	85493	169528	177703	127239	155409	132719	9562	57642	120941	284	63030	8326	352	535	1156	332041	60446	20774	569263	37915	95602	102941	13893	191240	11264	12914	83122	86221	98353	50421	584	14648	1916	521013	13514	9730	84095	58870	42581	150061	70282	28208	261620	17305	24745	34054	494486	4732	41867	10397	263	7524399	622595	687222	291132	832210	56996	880881	853619	111836	726892	603512	73683	1218982	398654	2497180	1108306	940871	2104912	239022	633845	7524399	8391638	20070143	7	5.3	546522	867239	152062	379625	179685	56246	246121	99717	374729	263656	3926227	12613979	12613979	6448602	6165377	977829
3	4133.0	585936	487005072197	613723856820	624450149363	6548612367402	40665	42463	83128	56491.338012	585936	316402	269534	1468305	59737	3.03	2.51	75281	803267	282862	5125816	2763015	3423250	3423250	1775199	1648051	9939670	14984858	9428446	5273287	15994410	540535	575794	8672285	647413	8288558	693040	652863	622949	602161	654907	696088	756439	556332	740359	711079	574407	426317	297361	348670	10667264	630616	434241	20875686	4066365	4142104	433652	1591194	5399203	20442034	951439	366646	868205	873200	1196903	1521148	970483	8152474	30738	13113	201778	241103	848135	3477	5399203	4060859	3663685	2732325	2666878	83128	53624.7587	8132434	7737236	561572	604324	667070	728403	671610	629910	591323	640640	666976	693978	574960	651105	617390	497457	358273	236773	224380	10208422	17479459	8404539	42.3	43866	43.9	55.4	52098	40.6	98640	212954	4819710	9694483	320999	306853	5579880	52390	572823	3221472	386749	50104	11742	11181203	770061	15476483	898239	2287450	433750	464489	898239	5193134	15219	15219	29913	7485	7734	1102107	1180118	16804719	1314483	16025794	1421443	1324473	1252859	1193484	1295547	1363064	1450417	1131292	1391464	1328469	1071864	784590	534134	573050	389.3	627852	20875686	2959407	2990780	1107202	139564	203442	252298	342663	283344	129690	129485	3407	78849	131796	30196	479436	544452	543860	408053	288055	142999	417240	573770	261579	85398	333616	2029	115622	143470	540715	227391	933973	104898	70517	460679	56767	240719	452235	0	207742	413860	143688	644510	279740	779970	766863	377196	100898	428649	183680	8535	297341	166979	48101	323008	451913	233004	178840	285711	217009	234897	250408	90659	40210	127174	79439	13767	21497	103498	109161	20707	16804719	64080	84617	112750	150048	139641	68081	53419	1152	40455	79640	18059	206336	237762	248795	195362	149440	70108	204783	289645	118846	39620	162626	1011	60130	70532	238120	80416	393622	42245	27259	185836	27665	135609	234795	0	103443	231898	69296	347422	155361	465971	402057	222582	59214	190835	81384	3488	138256	62390	20779	187750	229070	131273	90284	117945	108972	124281	117164	41705	15882	54240	35494	7223	4930	49689	35423	57	8152541	551136	659156	468362	154544	90042	163107	111977	692893	909338	452765	967498	802706	1652607	229713	1692280	1574873	2924108	1011180	720330	8152541	9790195	20875686	42	5.1	511224	1637654	351168	819037	78889	20495	557404	36949	600047	111697	5192714	15242062	15242062	7772764	7469298	1199573
4	3210.0	462360	315983343213	411902114464	418518490501	4125980999869	16036	16102	32138	45285.896438	462360	240313	222047	749637	61749	3.02	2.46	80493	806294	236846	3167219	1763503	1470953	1470953	765013	705940	6619516	9125523	6217293	3282447	9921694	335276	404682	5362136	433612	5076349	410643	397480	389122	375532	411895	447624	487329	355242	461925	408531	312098	231110	173218	235227	6642616	345422	268314	12992598	2573675	2724068	426311	1120165	987581	12566287	549498	299343	505620	485138	661013	933563	649666	5117219	13841	4066	72801	81708	393883	1223	987581	420059	2817515	484730	502851	32138	44742.7032	5010352	4726214	349790	417152	441081	428491	405440	392100	371081	405311	435283	463856	369830	429073	366389	265594	180573	120018	120580	6349982	10822050	5289824	41.4	46696	43.0	54.9	57362	39.8	126527	185452	2988934	3115252	161589	152711	3726095	18297	458294	1398152	232592	16338	3998	9877346	286064	12005017	410221	1270388	195431	214790	410221	3463374	5221	5221	32212	2622	2599	685066	821834	10372488	874693	9802563	839134	802920	781222	746613	817206	882907	951185	725072	890998	774920	577692	411683	293236	355807	290.4	314300	12992598	1653953	1457813	585404	162479	224406	22707	432943	210046	93639	202612	1754	84748	134652	24943	212966	62172	134424	548302	271213	517207	239410	553292	575890	903871	107575	38382	88283	504795	7672	14931	45556	6099	2557	6112	108325	136072	71952	14130	273695	196055	165969	5495	221381	17439	19227	190704	111879	144975	257849	36862	11142	18502	63122	23301	34327	146156	125056	113432	220217	317061	254390	17469	0	2886	271578	15924	0	143081	121146	29470	10372488	74277	98686	10729	196059	101319	45957	93730	645	45424	82738	12942	99476	28229	66028	267265	135979	258794	127103	278212	269579	435594	52290	18313	44342	263868	3585	6189	20132	2629	902	2884	53850	73109	39839	6765	137745	110110	84780	2896	116222	10022	10151	101801	66453	70968	124551	18223	5868	6096	31133	14285	17713	80722	66212	56799	116780	175679	129971	7816	0	1255	123001	7629	0	57457	25114	82	5117327	481070	210065	479229	139701	82571	185756	118011	193733	1067353	1083986	36321	506198	307545	287794	465570	1070547	1730882	522496	1039956	5117327	5703807	12992598	22	6.1	402223	586480	222750	599167	27840	4755	326829	6624	516090	45734	3463173	10297405	10297405	5261612	5035793	725053

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8
RegionAbbr
CA	67.320404	25.177160	-3.101485	-4.706037	4.407026	-5.775390	-2.811014	-0.146043
TX	43.191556	-10.699602	-15.687442	-0.315688	-10.453568	3.322438	3.345736	-4.351121
NY	26.419407	3.238080	15.933188	-5.934788	1.347212	10.766314	8.520713	0.926305
FL	31.432822	-12.900757	-0.393267	14.717084	10.657921	0.852195	2.207121	1.376193
PA	13.531901	-8.907294	10.182844	0.293992	-2.072087	-4.840718	-2.510874	-1.370546
IL	12.417125	-3.635323	4.527603	-0.628122	-3.899572	-0.172909	-2.054802	0.271814
MI	7.217125	-10.407115	5.212897	-2.265537	-0.960765	-4.309118	0.029509	2.223657
OH	10.338226	-12.537128	7.279680	-2.746292	-1.933483	-5.282655	-0.098395	2.661954
NJ	4.728497	7.498301	5.688313	3.692673	-2.532585	3.866985	-2.258937	-2.237845
GA	7.547692	-6.710732	-4.032778	-1.570728	-0.257958	5.436692	-5.449393	2.504245
NC	8.038400	-7.848564	-3.433742	-1.721384	2.439704	2.483189	-3.253880	1.830589
VA	4.503082	-0.000169	0.672323	1.913937	-3.322481	1.700228	-3.637186	-0.418838
MA	1.282166	6.055139	5.967845	4.164720	-2.779457	1.181995	-1.456220	-2.180047
MD	-0.441701	3.928366	2.364572	4.031252	-3.604813	2.952363	-3.518543	-0.148252
MO	-0.698659	-4.567963	0.932780	-1.230552	-0.061544	-1.911370	-0.223808	0.420068
WA	3.281982	3.186541	-0.230398	3.429841	-1.409232	-2.330611	-0.260901	2.168421
AZ	2.376126	0.789323	-5.752435	2.984429	2.584803	-0.532572	5.620356	1.256825
IN	-0.342637	-5.899252	2.172777	-2.984052	-1.289292	-3.436518	0.616144	1.555948
TN	0.079788	-5.418673	-0.295000	-2.671651	1.977797	0.962691	-2.925767	-0.271666
CO	-0.772431	2.227808	-1.598518	2.964669	-3.123626	-0.621338	0.963963	0.414342
SC	-3.111514	-3.274919	-1.991848	-1.300529	3.252373	2.473097	-2.832687	0.298146
CT	-5.413854	4.798944	3.710933	4.236997	-2.277963	0.544973	-1.859746	-2.321968
LA	-3.581901	-3.539782	-2.332202	-2.804084	2.367840	2.844284	-2.603643	0.000065
MN	-1.454759	-0.622348	0.768827	2.276174	-3.472325	-2.768814	1.574324	-0.337149
WI	-1.285486	-3.390711	2.253455	0.057863	-1.703981	-4.243503	1.522183	-0.247803
KY	-4.590302	-3.140127	-1.049884	-3.308739	2.250637	0.062161	-1.280529	-1.827113
AL	-3.570830	-4.164563	-1.423220	-3.164216	2.903233	2.307430	-3.509442	-0.372152
OR	-3.862786	1.427036	-0.443500	2.416649	1.343724	-1.949341	0.876820	-0.158645
OK	-4.495779	-1.361834	-3.105018	-3.951288	1.807075	-1.827505	3.818706	0.681093
MS	-7.501089	-2.003411	-2.217468	-3.664372	3.327493	2.845254	-2.441301	-0.122287
NV	-6.581356	2.652540	-2.832970	0.693487	0.786875	0.561275	1.432138	1.215541
UT	-6.831176	3.590845	-3.854658	0.185379	-3.358245	0.908831	0.786651	5.289378
KS	-7.122027	0.276723	-0.867313	-1.133046	-1.212334	-1.468594	1.855928	0.185332
IA	-7.047168	-0.692963	0.517779	-1.237969	-1.368690	-3.127834	2.332365	-1.073142
AR	-7.352917	-1.480066	-1.769965	-2.931082	2.997852	0.493442	-0.701777	-0.915410
NM	-8.160223	1.737526	-3.413789	-1.572444	2.912151	-0.121008	2.905147	-0.173036
HI	-8.395282	9.076429	-1.174982	1.055486	-0.345158	-1.317902	-2.715543	6.841207
NE	-9.341990	1.425450	-0.998756	-0.901640	-0.879588	-1.076031	2.153185	-0.367484
WV	-9.832298	-0.713482	-0.727067	-2.904878	3.543541	-0.540629	-0.484177	-4.985301
DC	-11.485659	5.451734	0.516645	1.684653	-2.798453	3.776787	-0.524282	2.941622
RI	-10.795428	3.588724	0.789578	1.185230	0.423904	0.171334	-0.456088	-1.892776
NH	-10.344747	3.464786	1.295601	2.833957	-0.707908	-0.410803	-0.556011	-2.257321
ID	-9.677239	1.792361	-1.934014	-1.027773	0.192167	-0.325320	1.408168	0.853828
DE	-11.094500	3.324309	0.118532	1.415230	0.149220	0.565963	-0.544958	-1.095819
ME	-10.374494	1.575919	0.810365	0.686126	2.447631	-1.300093	0.302217	-3.736477
MT	-10.774798	1.862764	-0.845265	-0.572628	1.772330	-1.109617	1.917061	-1.859826
AK	-10.880967	4.775845	-3.105081	0.538624	-2.186515	1.112020	2.604951	4.029744
VT	-11.859212	2.796808	0.569223	0.907925	1.506416	-0.579738	-0.021773	-3.186915
SD	-11.292890	2.405303	-1.424176	-0.907701	0.628139	-0.664910	1.986350	-0.698878
WY	-11.878690	3.039309	-1.161336	-0.089128	0.301224	0.210282	0.711577	-0.421634
ND	-11.459512	2.752705	-1.088186	-0.120025	-0.316664	-0.357381	1.500362	-0.770826

	Provider_Count	AAGEBASECY	AGGDI_CY	AGGHINC_CY	AGGINC_CY	AGGNW_CY	AIFBASE_CY	AIMBASE_CY	AMERIND_CY	AREA	ASIAN_CY	ASNFBASECY	ASNMBASECY	ASSCDEG_CY	AVGDI_CY	AVGFMSZ_CY	AVGHHSZ_CY	AVGHINC_CY	AVGNW_CY	AVGVAL_CY	BABYBOOMCY	BACHDEG_CY	BAGEBASECY	BLACK_CY	BLKFBASECY	BLKMBASECY	CIVLBFR_CY	EDUCBASECY	EMP_CY	FAMHH_CY	FAMPOP_CY	FEM0_CY	FEM15_CY	FEM18UP_CY	FEM20_CY	FEM21UP_CY	FEM25_CY	FEM30_CY	FEM35_CY	FEM40_CY	FEM45_CY	FEM50_CY	FEM55_CY	FEM5_CY	FEM60_CY	FEM65_CY	FEM70_CY	FEM75_CY	FEM80_CY	FEM85_CY	FEMALES_CY	GED_CY	GENALPHACY	GENBASE_CY	GENX_CY	GENZ_CY	GQPOP_CY	GRADDEG_CY	HAGEBASECY	HHPOP_CY	HINC0_CY	HINC150_CY	HINC15_CY	HINC25_CY	HINC35_CY	HINC50_CY	HINC75_CY	HINCBASECY	HISPAI_CY	HISPASN_CY	HISPBLK_CY	HISPMLT_CY	HISPOTH_CY	HISPPI_CY	HISPPOP_CY	HISPWHT_CY	HSGRAD_CY	HSPFBASECY	HSPMBASECY	IAGEBASECY	LANDAREA	MAL18UP_CY	MAL21UP_CY	MALE0_CY	MALE15_CY	MALE20_CY	MALE25_CY	MALE30_CY	MALE35_CY	MALE40_CY	MALE45_CY	MALE50_CY	MALE55_CY	MALE5_CY	MALE60_CY	MALE65_CY	MALE70_CY	MALE75_CY	MALE80_CY	MALE85_CY	MALES_CY	MARBASE_CY	MARRIED_CY	MEDAGE_CY	MEDDI_CY	MEDFAGE_CY	MEDHHR_CY	MEDHINC_CY	MEDMAGE_CY	MEDNW_CY	MEDVAL_CY	MILLENN_CY	MINORITYCY	MLTFBASECY	MLTMBASECY	NEVMARR_CY	NHSPAI_CY	NHSPASN_CY	NHSPBLK_CY	NHSPMLT_CY	NHSPOTH_CY	NHSPPI_CY	NHSPWHT_CY	NOHS_CY	NONHISP_CY	OAGEBASECY	OLDRGENSCY	OTHFBASECY	OTHMBASECY	OTHRACE_CY	OWNER_CY	PACIFIC_CY	PAGEBASECY	PCI_CY	PIFBASE_CY	PIMBASE_CY	POP0_CY	POP15_CY	POP18UP_CY	POP20_CY	POP21UP_CY	POP25_CY	POP30_CY	POP35_CY	POP40_CY	POP45_CY	POP50_CY	POP55_CY	POP5_CY	POP60_CY	POP65_CY	POP70_CY	POP75_CY	POP80_CY	POP85_CY	POPDENS_CY	RACE2UP_CY	RACEBASECY	RENTER_CY	SMCOLL_CY	SOMEHS_CY	TADULT01	TADULT02	TADULT03	TADULT04	TADULT05	TADULT06	TADULT07	TADULT08	TADULT09	TADULT11	TADULT12	TADULT13	TADULT14	TADULT15	TADULT16	TADULT17	TADULT18	TADULT19	TADULT20	TADULT21	TADULT22	TADULT23	TADULT24	TADULT25	TADULT26	TADULT27	TADULT28	TADULT29	TADULT30	TADULT31	TADULT32	TADULT33	TADULT34	TADULT35	TADULT36	TADULT37	TADULT38	TADULT39	TADULT40	TADULT41	TADULT42	TADULT43	TADULT44	TADULT45	TADULT46	TADULT47	TADULT48	TADULT49	TADULT50	TADULT51	TADULT52	TADULT53	TADULT54	TADULT55	TADULT56	TADULT57	TADULT58	TADULT59	TADULT60	TADULT61	TADULT62	TADULT63	TADULT64	TADULT65	TADULT66	TADULT67	TADULT68	TADULTBASE	THH01	THH02	THH03	THH04	THH05	THH06	THH07	THH08	THH09	THH11	THH12	THH13	THH14	THH15	THH16	THH17	THH18	THH19	THH20	THH21	THH22	THH23	THH24	THH25	THH26	THH27	THH28	THH29	THH30	THH31	THH32	THH33	THH34	THH35	THH36	THH37	THH38	THH39	THH40	THH41	THH42	THH43	THH44	THH45	THH46	THH47	THH48	THH49	THH50	THH51	THH52	THH53	THH54	THH55	THH56	THH57	THH58	THH59	THH60	THH61	THH62	THH63	THH64	THH65	THH66	THH67	THH68	THHBASE	THHGRPL1	THHGRPL11	THHGRPL12	THHGRPL13	THHGRPL14	THHGRPL2	THHGRPL3	THHGRPL4	THHGRPL5	THHGRPL6	THHGRPL7	THHGRPL8	THHGRPL9	THHGRPU1	THHGRPU2	THHGRPU3	THHGRPU4	THHGRPU5	THHGRPU6	TOTHH_CY	TOTHU_CY	TOTPOP_CY	TSEGNUM	UNEMPRT_CY	UNEMP_CY	VACANT_CY	VAL0_CY	VAL150K_CY	VAL1M_CY	VAL1PT5MCY	VAL250K_CY	VAL2M_CY	VAL50K_CY	VAL750K_CY	VALBASE_CY	WAGEBASECY	WHITE_CY	WHTFBASECY	WHTMBASECY	WIDOWED_CY
0	0.067079	0.058098	0.067967	0.067621	0.067624	0.067112	0.036234	0.037716	0.036973	0.010595	0.058098	0.057994	0.058208	0.067235	0.014692	0.027737	0.025862	0.014430	0.009782	0.017051	0.067878	0.067814	0.053549	0.053549	0.052824	0.054324	0.068043	0.068133	0.068035	0.068029	0.068037	0.067392	0.067874	0.068112	0.067946	0.068110	0.067880	0.067885	0.067926	0.067998	0.068053	0.068018	0.067958	0.067525	0.067857	0.067614	0.067222	0.066847	0.066401	0.065686	0.068118	0.063479	0.067358	0.068125	0.068075	0.067689	0.066467	0.066317	0.062484	0.068126	0.066208	0.066029	0.066431	0.066181	0.066665	0.067362	0.067875	0.067948	0.059589	0.055683	0.051291	0.062407	0.059857	0.052641	0.062484	0.060889	0.066250	0.062488	0.062474	0.036973	0.010548	0.068157	0.068160	0.067402	0.067866	0.067924	0.067860	0.067865	0.067921	0.068015	0.068094	0.068070	0.067994	0.067524	0.067902	0.067688	0.067248	0.066776	0.066235	0.065636	0.068121	0.068141	0.068104	-0.008828	0.009330	-0.007062	-0.006297	0.006627	-0.011373	-0.003487	0.012110	0.067917	0.065704	0.064007	0.063799	0.067857	0.017708	0.058119	0.052785	0.063430	0.056606	0.032225	0.064021	0.064803	0.066425	0.060082	0.066610	0.059936	0.060213	0.060082	0.067147	0.034347	0.034347	0.004348	0.034759	0.033930	0.067397	0.067872	0.068141	0.067940	0.068141	0.067874	0.067880	0.067930	0.068012	0.068078	0.068048	0.067980	0.067525	0.067885	0.067654	0.067242	0.066832	0.066369	0.065761	-0.005488	0.063908	0.068125	0.067652	0.067676	0.067310	0.055469	0.058244	0.055469	0.055196	0.061669	0.054852	0.045551	0.044954	0.050047	0.059840	0.049609	0.058228	0.058247	0.032323	0.048176	0.058037	0.027361	0.036779	0.043737	0.037506	0.020139	0.050777	0.001219	0.036922	0.023664	0.046224	0.050045	0.056971	0.043543	0.046956	0.042867	0.046537	0.053831	0.052349	0.029883	0.048035	0.057004	0.034282	0.034117	0.055958	0.030280	0.038379	0.051657	0.060586	0.026127	0.022421	0.010628	0.045219	0.008275	0.022589	0.048960	0.056922	0.052426	0.036720	0.040956	0.024883	0.035746	0.026889	0.049271	0.047622	0.056287	0.048052	0.021827	0.049742	0.058882	0.061360	0.060507	0.068141	0.056054	0.057746	0.054611	0.054877	0.062198	0.055330	0.045132	0.045441	0.050367	0.059038	0.049893	0.057119	0.057731	0.031374	0.047457	0.057408	0.026786	0.035501	0.043122	0.036897	0.019834	0.050372	0.000097	0.036246	0.022835	0.044746	0.050393	0.056702	0.042578	0.047109	0.042227	0.046101	0.054112	0.051746	0.029738	0.047725	0.056214	0.033174	0.034145	0.056224	0.030600	0.037866	0.051180	0.059060	0.025046	0.021073	0.009522	0.044457	0.006334	0.022736	0.047624	0.056655	0.051269	0.036442	0.039861	0.024217	0.034967	0.026457	0.049263	0.047824	0.054559	0.047645	0.022017	0.046895	0.058053	0.057093	0.054201	0.067948	0.064084	0.060069	0.034802	0.056153	0.060691	0.052587	0.056097	0.053469	0.048462	0.036268	0.057089	0.064164	0.047533	0.050115	0.065278	0.058679	0.066223	0.050364	0.037789	0.067948	0.067751	0.068125	-0.002565	0.013824	0.067448	0.060060	0.048786	0.050060	0.053909	0.052876	0.061415	0.053834	0.043232	0.054522	0.067146	0.067075	0.067075	0.066984	0.067158	0.067143
1	-0.003570	0.078358	0.007632	0.013815	0.013850	0.007650	0.032811	0.033594	0.033203	0.011870	0.078358	0.078733	0.077934	-0.011698	0.086543	0.058789	0.047331	0.087002	0.062984	0.107941	-0.009610	0.009897	-0.059093	-0.059093	-0.059540	-0.058562	-0.000099	-0.002924	0.000294	-0.009092	-0.000135	0.001447	0.000411	-0.003220	0.001643	-0.003320	0.006778	0.006489	0.002683	-0.000206	-0.001863	-0.003488	-0.006710	-0.000264	-0.009542	-0.013022	-0.014909	-0.015643	-0.015773	-0.011326	-0.002369	-0.050427	0.001937	-0.001592	-0.000534	0.000631	-0.010569	0.016594	0.038929	-0.001388	-0.031004	0.031528	-0.034124	-0.037422	-0.033052	-0.024669	-0.010650	-0.011740	0.061031	0.081668	0.002465	0.058781	0.063432	0.075984	0.038929	0.017741	-0.028801	0.038922	0.038932	0.033203	0.012102	-0.001403	-0.001544	0.001788	0.001720	0.003512	0.007357	0.007864	0.004619	0.001246	-0.000635	-0.002192	-0.005564	0.000285	-0.008973	-0.013372	-0.015794	-0.015676	-0.013866	-0.005600	-0.000795	-0.002121	-0.003418	-0.023117	0.091761	-0.026162	-0.017788	0.090619	-0.019082	0.044550	0.108742	0.005450	0.028642	0.048676	0.049491	0.008129	0.012079	0.078280	-0.061888	0.039897	0.034381	0.086034	-0.040299	0.035399	-0.023884	0.063222	-0.013902	0.064405	0.062076	0.063222	-0.026175	0.085895	0.085895	0.068772	0.086375	0.085400	0.001621	0.001083	-0.002333	0.002598	-0.002455	0.007075	0.007187	0.003660	0.000516	-0.001254	-0.002849	-0.006155	0.000016	-0.009272	-0.013187	-0.015317	-0.015662	-0.014967	-0.009227	0.025373	0.049081	-0.001592	0.008534	-0.004011	-0.006131	0.071942	-0.010463	0.010224	0.016941	0.055896	0.088235	0.084986	0.102180	0.070966	0.010944	0.092762	-0.036219	-0.015724	-0.099459	-0.083461	-0.050462	0.019262	-0.100383	-0.097887	-0.096308	-0.099094	0.041416	-0.019879	-0.056383	-0.104982	-0.027970	0.087403	-0.001613	-0.012517	0.088596	-0.031808	0.094322	-0.036517	-0.043726	0.039763	0.044789	-0.064166	-0.103474	-0.033358	0.027259	-0.025306	-0.019744	-0.044603	0.012888	-0.094829	-0.094625	-0.041227	-0.053335	-0.067892	0.022370	-0.050605	-0.013456	-0.051233	-0.103788	-0.077216	-0.099452	-0.111264	-0.117810	0.093961	0.089553	0.023501	0.070591	0.018706	0.039119	-0.056989	-0.037168	0.043876	-0.002333	0.070589	-0.013876	0.004464	0.012156	0.052247	0.086852	0.084383	0.101932	0.068955	0.008663	0.092922	-0.043486	-0.019348	-0.101269	-0.085413	-0.055806	0.016646	-0.100524	-0.099004	-0.097437	-0.099144	0.040599	-0.018370	-0.055940	-0.103951	-0.033310	0.086692	-0.005427	-0.015858	0.087950	-0.032558	0.093337	-0.041952	-0.048266	0.039260	0.041022	-0.067203	-0.102147	-0.033188	0.024481	-0.023861	-0.020854	-0.046268	0.009024	-0.094621	-0.094054	-0.040291	-0.056039	-0.065157	0.022911	-0.053729	-0.017638	-0.053793	-0.105655	-0.080008	-0.098469	-0.111256	-0.118202	0.093789	0.088934	0.014826	0.068402	0.018833	0.044587	-0.060631	-0.054828	0.014479	-0.011740	0.030154	-0.041832	-0.118923	0.080793	-0.047940	0.093005	0.067507	-0.062549	-0.080377	-0.093682	0.011350	0.010511	-0.019762	0.054064	0.030386	-0.071277	0.012321	-0.094254	-0.115470	-0.011740	-0.016480	-0.001592	-0.055622	-0.026290	-0.007574	-0.055116	-0.101505	-0.105472	0.088038	0.090609	-0.044032	0.086213	-0.116322	0.088957	-0.026186	-0.023713	-0.023713	-0.024738	-0.022661	-0.019307
2	0.032090	0.003081	0.008313	0.016172	0.016107	0.028764	-0.077737	-0.077960	-0.077859	-0.091612	0.003081	0.002646	0.003561	0.016275	0.040054	-0.049858	-0.061223	0.064525	0.086860	0.037786	0.015549	0.011148	0.010890	0.010890	0.013322	0.008180	0.006992	0.005943	0.006476	-0.000274	-0.005955	-0.023959	-0.004520	0.006753	-0.003744	0.007116	-0.008720	-0.006691	-0.006329	-0.003357	0.003477	0.011469	0.014992	-0.020271	0.016807	0.016619	0.019803	0.024402	0.033593	0.050590	0.001376	-0.003323	-0.024446	-0.000445	0.000996	-0.014853	0.033545	0.037858	-0.058942	-0.001215	0.009746	0.022930	0.001435	-0.003379	-0.003579	0.000194	0.010974	0.007980	-0.048277	-0.025747	0.078591	-0.027973	-0.042775	-0.031861	-0.058942	-0.075435	0.027691	-0.058087	-0.059783	-0.077859	-0.091921	0.002397	0.002713	-0.023791	-0.006228	-0.007188	-0.012136	-0.010003	-0.008991	-0.005708	0.001410	0.009458	0.013994	-0.020048	0.016063	0.014682	0.015421	0.018300	0.025710	0.037889	-0.002312	0.004034	-0.001162	0.101486	0.043127	0.103668	0.092603	0.057047	0.098614	0.072006	0.029761	-0.008161	-0.036387	-0.012029	-0.015629	0.011553	-0.076563	0.003546	0.006537	-0.001932	0.065062	-0.043034	0.045816	-0.034429	0.031930	-0.041085	0.029334	-0.039348	-0.042746	-0.041085	0.006572	-0.042411	-0.042411	0.081799	-0.041946	-0.042868	-0.023873	-0.005398	0.004626	-0.005503	0.004972	-0.010467	-0.008372	-0.007672	-0.004527	0.002451	0.010478	0.014510	-0.020158	0.016455	0.015716	0.017793	0.021683	0.030243	0.045972	0.020974	-0.013808	-0.000445	0.009770	-0.018910	-0.016641	0.055604	-0.019444	-0.111625	0.082507	-0.001303	0.013034	0.097352	0.010728	-0.011326	-0.015736	0.051085	-0.063356	-0.080846	-0.081298	0.081995	0.028746	0.118725	0.066486	0.077051	0.063482	0.105451	-0.000352	-0.028090	0.007668	0.091566	-0.140576	-0.019441	-0.088123	-0.128073	-0.026115	-0.119573	0.076317	-0.010949	-0.096141	0.111727	0.032838	-0.001280	0.062667	-0.026991	0.105633	-0.010747	-0.036067	0.068089	0.090062	-0.079574	-0.039653	-0.090367	-0.112380	-0.064592	0.136751	-0.123052	-0.087573	0.037244	0.059572	0.022237	0.079540	0.025796	0.024162	0.050945	-0.016170	-0.103065	0.051743	0.118450	-0.086586	-0.006524	0.021003	-0.011673	0.004626	0.053026	-0.019005	-0.113898	0.082707	-0.003554	0.011146	0.099461	0.013610	-0.011029	-0.019594	0.048780	-0.061826	-0.081416	-0.079944	0.081974	0.025344	0.120533	0.071711	0.079021	0.064313	0.105245	0.000466	-0.027910	0.008417	0.092725	-0.141759	-0.020136	-0.090154	-0.128668	-0.026564	-0.120108	0.079377	-0.013081	-0.095964	0.112286	0.035718	0.001598	0.068500	-0.026416	0.104955	-0.011142	-0.034554	0.066114	0.099710	-0.076920	-0.038922	-0.086326	-0.108512	-0.064144	0.137573	-0.123816	-0.089430	0.038849	0.061339	0.027616	0.083932	0.033106	0.028594	0.049931	-0.016009	-0.109768	0.055062	0.118193	-0.090465	-0.022624	-0.028570	0.045168	0.007981	-0.001695	-0.000489	0.057746	0.030119	-0.036581	0.044748	0.048158	-0.082089	0.093889	0.080423	-0.111393	0.055943	0.021591	0.078770	-0.031582	0.024465	0.007623	-0.022470	-0.006959	0.007981	0.008596	-0.000445	-0.098971	0.011121	0.016734	0.013036	-0.041565	-0.001591	0.008349	0.010803	0.003977	0.023723	-0.012311	0.013686	0.006555	0.008716	0.008716	0.010216	0.007182	0.018853
3	-0.006155	-0.047969	-0.000254	-0.004334	-0.004376	0.027317	-0.042152	-0.041447	-0.041809	-0.019525	-0.047969	-0.047092	-0.048935	0.010362	0.146194	-0.010287	-0.004217	0.134147	0.189814	0.093224	0.002748	0.002926	0.002363	0.002363	0.000660	0.004253	-0.008931	-0.004438	-0.008959	-0.005501	-0.012409	-0.022309	-0.019915	-0.006062	-0.019494	-0.005253	-0.018465	-0.018718	-0.016942	-0.013434	-0.009514	-0.006287	-0.003280	-0.020423	0.001242	0.010229	0.017504	0.021941	0.023101	0.022084	-0.009265	-0.004943	-0.022865	-0.009664	-0.011066	-0.020331	-0.022888	0.001111	-0.003358	-0.009359	-0.020401	-0.007694	-0.006811	0.001988	0.007043	0.004048	0.000094	-0.003046	-0.039888	-0.037061	0.031089	-0.020490	-0.044730	-0.024271	-0.003358	0.027415	0.000950	-0.002142	-0.004562	-0.041809	-0.020049	-0.006743	-0.005900	-0.022266	-0.019927	-0.019534	-0.017150	-0.017661	-0.016810	-0.013914	-0.009778	-0.007283	-0.005016	-0.020905	-0.001785	0.009560	0.021204	0.029632	0.035902	0.039008	-0.010072	-0.007005	-0.006305	0.075748	0.139980	0.070865	0.042360	0.141307	0.078435	0.155997	0.096154	-0.018306	-0.012687	-0.021383	-0.021127	-0.016204	-0.034011	-0.048131	0.000554	-0.021317	0.019147	-0.013559	-0.004754	-0.031757	-0.012467	-0.043820	0.026258	-0.043958	-0.043681	-0.043820	0.007471	-0.014642	-0.014642	0.137306	-0.015367	-0.013916	-0.022287	-0.019922	-0.006395	-0.019516	-0.005569	-0.017794	-0.018183	-0.016877	-0.013674	-0.009646	-0.006778	-0.004121	-0.020668	-0.000197	0.009917	0.019206	0.025381	0.028586	0.028365	0.041546	-0.021258	-0.009664	-0.017578	-0.006601	-0.025659	-0.011353	0.033891	0.002805	0.054170	-0.000644	-0.016796	-0.029490	-0.073561	0.011651	0.010343	-0.070535	0.036005	0.087234	0.008373	0.003913	0.025553	0.116382	-0.020935	0.020989	-0.019909	-0.089950	0.061997	-0.039389	0.016964	-0.086284	0.029623	-0.050788	0.089029	-0.010764	-0.032386	0.047983	-0.032208	0.080246	0.089166	-0.094979	0.090853	0.049068	-0.082718	0.195664	0.058907	0.199108	0.173721	0.117937	-0.001166	-0.065312	-0.094514	-0.080855	0.035077	-0.069519	-0.056121	0.045844	0.104736	0.052091	0.004193	0.024630	-0.074103	-0.070036	-0.028931	-0.047287	-0.060408	-0.005888	0.009961	-0.080290	-0.022494	-0.052943	-0.023672	-0.034677	-0.006395	-0.011440	0.032828	0.004564	0.055371	0.002478	-0.017497	-0.026184	-0.075106	0.012620	0.011499	-0.069699	0.032877	0.087607	0.001183	0.002847	0.024097	0.115283	-0.025958	0.018690	-0.022541	-0.090125	0.063077	-0.037893	0.017510	-0.086672	0.029628	-0.049803	0.090620	-0.010091	-0.029382	0.045939	-0.030108	0.078099	0.085495	-0.094622	0.093196	0.049138	-0.086343	0.195697	0.060200	0.199201	0.175298	0.120710	0.001720	-0.069856	-0.098154	-0.082756	0.031807	-0.077156	-0.055377	0.042269	0.104069	0.052880	-0.006087	0.008904	-0.076142	-0.071180	-0.037057	-0.043436	-0.059180	-0.002454	0.013795	-0.079867	-0.028309	-0.050283	-0.002161	-0.070956	-0.003046	0.017808	0.031465	-0.059071	-0.054671	-0.032490	-0.029284	-0.055731	0.042425	0.029325	-0.057323	0.016255	-0.001403	0.164185	-0.065879	-0.009064	0.022195	0.050779	0.009263	-0.089657	-0.003046	0.003692	-0.009664	-0.101031	-0.014716	-0.008299	0.060365	-0.029720	0.029495	-0.044447	-0.048578	0.091377	-0.047359	-0.043359	-0.034301	0.007493	0.005562	0.005562	0.006448	0.004655	0.005683
4	-0.008597	0.017211	-0.012733	-0.013576	-0.013243	-0.003663	0.046590	0.045204	0.045911	-0.055925	0.017211	0.018703	0.015560	0.022603	-0.168747	-0.078060	-0.054355	-0.162045	-0.113371	-0.056396	0.009111	-0.011294	0.007543	0.007543	0.007419	0.007676	-0.013265	0.001715	-0.013815	-0.006325	-0.009482	-0.023116	-0.018770	0.000337	-0.012778	0.001248	-0.008391	-0.009869	-0.013597	-0.011485	-0.006375	-0.002556	0.001037	-0.023760	0.008764	0.019648	0.028539	0.033154	0.033254	0.026726	-0.004739	0.002702	-0.023419	-0.005365	-0.008002	-0.021778	-0.013627	-0.011731	-0.004825	-0.005175	0.022096	-0.027388	0.019121	0.012316	0.009339	-0.002924	-0.010882	-0.003012	0.003575	0.041749	0.037616	0.014402	0.003342	0.040255	-0.004825	-0.014453	0.004269	-0.004008	-0.005633	0.045911	-0.056254	-0.000877	-0.000008	-0.022975	-0.018050	-0.012096	-0.007847	-0.008972	-0.012439	-0.011073	-0.006481	-0.003508	-0.001830	-0.023909	0.004274	0.016670	0.028889	0.037673	0.042740	0.044746	-0.006007	-0.001138	-0.007947	0.114445	-0.176215	0.117680	0.142872	-0.173584	0.109373	-0.152659	-0.060321	-0.010405	0.003267	0.017081	0.015596	-0.001121	0.057809	0.016809	0.005564	0.017496	0.022014	0.023370	-0.015896	0.007299	-0.005285	0.003686	0.034618	0.004747	0.002666	0.003686	-0.010207	0.025096	0.025096	-0.144260	0.025219	0.024968	-0.023044	-0.018401	-0.000256	-0.012430	0.000636	-0.008113	-0.009415	-0.013014	-0.011280	-0.006428	-0.003025	-0.000352	-0.023836	0.006630	0.018259	0.028703	0.035180	0.037328	0.033419	-0.049979	0.016349	-0.005365	0.007035	0.002727	0.019080	-0.019530	-0.118887	-0.080711	-0.096373	0.031761	0.027145	0.010050	0.061061	-0.040542	-0.100661	0.042381	-0.063219	-0.033547	0.029744	-0.040704	-0.019747	-0.109123	-0.022691	0.039244	-0.109578	-0.058600	0.089820	-0.116917	-0.014023	-0.065867	-0.064111	0.062411	0.050934	-0.114977	0.082107	-0.079075	0.017148	0.020737	-0.035608	0.034151	-0.008905	0.047490	-0.026527	0.160520	0.024531	0.168166	0.166375	0.071135	0.009042	0.047640	0.042366	-0.018921	0.064635	0.116193	0.002635	-0.060148	0.027425	0.055610	0.019440	0.011440	-0.047201	0.040203	0.027668	0.038156	0.049925	-0.058586	0.018342	0.019774	-0.021324	-0.044134	-0.065348	-0.003765	-0.000256	-0.021373	-0.123220	-0.087982	-0.101902	0.031021	0.026945	0.005890	0.060968	-0.044113	-0.104463	0.042848	-0.068494	-0.038668	0.027978	-0.041280	-0.020361	-0.109541	-0.025014	0.038582	-0.109541	-0.058810	0.089594	-0.113137	-0.013018	-0.066762	-0.067651	0.060233	0.046920	-0.118387	0.082582	-0.083217	0.015438	0.017834	-0.041346	0.033575	-0.012720	0.045688	-0.025029	0.160649	0.024782	0.167494	0.167288	0.072863	0.006039	0.048792	0.040811	-0.013280	0.069398	0.112606	0.000003	-0.067300	0.020960	0.053715	0.013426	0.002533	-0.046207	0.040680	0.024884	0.036265	0.048336	-0.070784	0.014260	0.019689	-0.020158	-0.036796	-0.068038	-0.004876	-0.003012	-0.066280	-0.010118	-0.001907	0.023294	-0.049398	0.012805	-0.008549	-0.028384	-0.036217	-0.071715	-0.025471	0.010444	0.133500	-0.001523	0.008921	-0.011950	-0.024663	0.068124	-0.001672	-0.003012	0.004411	-0.005365	0.181867	0.045235	-0.002658	0.066810	-0.008848	-0.036772	0.053907	0.057481	-0.027950	0.065311	-0.043220	0.043804	-0.010207	-0.016435	-0.016435	-0.016171	-0.016702	0.023654

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8
RegionAbbr
CA	24.941061	6.474051	5.609687	-0.962691	-0.704153	0.894702	0.912505	-0.478776
TX	23.908717	-2.544515	5.188710	0.814537	1.263244	-0.390794	0.796420	-1.510516
NY	16.397040	4.697254	-4.149995	-0.055749	0.600847	5.558235	3.165286	-0.460776
FL	21.575314	-0.545527	-1.160204	-1.382744	-4.993933	0.063733	-2.346939	0.841382
PA	14.254328	-0.192569	-7.431125	-2.612835	0.542363	2.054582	2.224964	-1.109572
IL	13.903033	1.648756	-2.133280	1.190061	3.743880	0.313898	1.203285	-0.065390
MI	10.537042	-4.242443	-4.319592	-1.417743	0.299792	3.046424	-0.083627	0.379641
OH	11.340141	-5.463046	-5.949948	-0.040940	2.577762	0.054191	0.904389	0.531492
NJ	6.359957	11.923885	-1.803437	2.064464	0.032182	0.124552	-0.960482	-0.039266
GA	10.837933	-2.252931	1.608639	4.604972	-0.408737	-2.809584	-1.003404	-1.468180
NC	11.626048	-4.611761	0.913803	1.403094	-2.432750	-0.588660	0.045871	-1.422783
VA	9.744681	2.424011	-0.872190	1.231040	0.159863	-3.181201	3.037987	-1.743821
MA	4.507952	11.264660	-4.467088	-1.673932	0.368888	1.671717	0.221221	-1.042171
MD	4.452790	7.260558	-1.979991	2.836840	1.174696	-3.648621	-0.409089	-1.969792
MO	4.902518	-4.333476	-2.138973	-0.195669	0.461320	-1.158055	1.765426	-0.031696
WA	9.147755	4.068888	3.896582	-4.377601	-1.041934	-1.104182	2.407585	-0.373697
AZ	8.454773	1.349003	6.549264	0.354253	-2.795823	2.677352	-3.556716	1.030261
IN	3.858799	-6.668229	-2.547097	0.339778	2.779614	-0.741004	-1.179909	2.625238
TN	5.239647	-5.048140	-1.881375	2.747517	-1.342516	-2.050955	-0.197252	0.895178
CO	5.947022	3.222646	4.968447	-2.856916	1.318518	-0.989827	-1.125881	-0.443467
SC	1.718689	-4.691551	-0.619011	3.274218	-3.414196	-2.499577	-0.623431	0.431597
CT	-2.294625	10.431394	-4.872230	0.092104	0.589346	-0.159243	-3.347191	-1.503408
LA	1.056402	-3.511091	-0.672560	4.889602	-0.827736	-0.069310	-0.055843	-2.734135
MN	4.113591	-0.888401	-0.001173	-5.122606	3.598770	-1.222785	-1.959676	-1.393251
WI	3.767021	-3.213935	-3.497921	-4.185546	3.481066	-0.207978	-1.939934	0.580935
KY	-0.522537	-6.035116	-2.311867	1.603457	-0.983441	-1.515464	0.491492	0.730095
AL	0.925128	-5.318121	-1.607998	4.253343	-1.751460	-1.038961	0.146819	-0.091159
OR	2.338167	2.398451	2.504058	-6.288424	-2.015892	-0.349357	-1.258296	1.892730
OK	0.294842	-6.532883	3.154125	1.527624	0.665162	2.570006	0.153812	0.534761
MS	-5.866029	-5.675623	-0.737126	6.285331	-1.565272	-0.573433	0.422939	-1.234640
NV	-0.838720	3.135504	6.268458	1.763869	-1.500820	0.511188	-2.649213	3.706389
UT	-2.881539	2.169321	8.881553	-0.298284	2.595103	-3.469909	0.240853	1.380089
KS	-2.902079	-3.822638	1.551843	0.804892	4.384112	1.185009	-0.163252	1.509428
IA	-4.339032	-4.497665	-1.166603	-2.728882	4.526969	-0.478473	-0.095412	3.695027
AR	-2.980338	-5.599416	1.106490	3.101318	-2.470753	-0.025113	0.099192	2.347363
NM	-4.288426	-0.981584	6.037603	-0.322682	-3.979422	5.608560	-2.521629	-0.211333
HI	-10.161014	10.925445	2.845990	0.886577	-1.758315	-1.406064	7.962511	5.360556
NE	-7.821808	-2.790074	1.732173	0.295374	5.522137	0.515462	-1.494783	1.140025
WV	-12.870474	-5.544047	-6.308938	-0.439109	-4.314826	0.774787	1.414517	0.949638
DC	-20.917248	9.532703	-3.460152	10.923909	2.896917	3.413946	-0.107010	0.506364
RI	-12.154689	9.182793	-3.332281	-0.125686	-0.243982	1.736370	-3.376211	1.001130
NH	-12.059679	3.929578	-3.747129	-5.235653	-1.495788	-3.763940	-1.271458	-0.821153
ID	-8.787058	-2.575458	4.857307	-2.429535	0.472843	0.120727	0.485380	1.504048
DE	-11.754908	4.623053	-2.260126	1.744238	-1.619071	-3.778306	-3.127486	0.162652
ME	-12.797534	-0.946150	-4.928566	-5.514554	-4.260834	-0.207112	0.788516	-0.327974
MT	-13.697272	-4.371800	1.044493	-2.815766	-2.016772	3.464333	1.947298	-1.906533
AK	-15.516930	1.766643	10.256209	-0.055205	0.214877	-0.055887	2.102388	-4.804770
VT	-19.784226	-0.583679	-5.201189	-3.978771	-1.249397	-1.050658	0.374199	-0.682257
SD	-15.690296	-4.442951	1.937501	-1.077048	1.393601	1.940808	0.448964	-1.634963
WY	-18.359159	-0.995520	2.587292	-1.794674	0.298392	-0.320102	1.084521	-1.805239
ND	-16.864769	-3.508255	2.058941	-1.043167	3.225556	0.553975	0.005788	-2.425301

	Provider_Count	AAGEBASECY	AGGDI_CY	AGGHINC_CY	AGGINC_CY	AGGNW_CY	AIFBASE_CY	AIMBASE_CY	AMERIND_CY	AREA	ASIAN_CY	ASNFBASECY	ASNMBASECY	ASSCDEG_CY	AVGDI_CY	AVGFMSZ_CY	AVGHHSZ_CY	AVGHINC_CY	AVGNW_CY	AVGVAL_CY	BABYBOOMCY	BACHDEG_CY	BAGEBASECY	BLACK_CY	BLKFBASECY	BLKMBASECY	CIVLBFR_CY	EDUCBASECY	EMP_CY	FAMHH_CY	FAMPOP_CY	FEM0_CY	FEM15_CY	FEM18UP_CY	FEM20_CY	FEM21UP_CY	FEM25_CY	FEM30_CY	FEM35_CY	FEM40_CY	FEM45_CY	FEM50_CY	FEM55_CY	FEM5_CY	FEM60_CY	FEM65_CY	FEM70_CY	FEM75_CY	FEM80_CY	FEM85_CY	FEMALES_CY	GED_CY	GENALPHACY	GENBASE_CY	GENX_CY	GENZ_CY	GQPOP_CY	GRADDEG_CY	HAGEBASECY	HHPOP_CY	HINC0_CY	HINC150_CY	HINC15_CY	HINC25_CY	HINC35_CY	HINC50_CY	HINC75_CY	HINCBASECY	HISPAI_CY	HISPASN_CY	HISPBLK_CY	HISPMLT_CY	HISPOTH_CY	HISPPI_CY	HISPPOP_CY	HISPWHT_CY	HSGRAD_CY	HSPFBASECY	HSPMBASECY	IAGEBASECY	LANDAREA	MAL18UP_CY	MAL21UP_CY	MALE0_CY	MALE15_CY	MALE20_CY	MALE25_CY	MALE30_CY	MALE35_CY	MALE40_CY	MALE45_CY	MALE50_CY	MALE55_CY	MALE5_CY	MALE60_CY	MALE65_CY	MALE70_CY	MALE75_CY	MALE80_CY	MALE85_CY	MALES_CY	MARBASE_CY	MARRIED_CY	MEDAGE_CY	MEDDI_CY	MEDFAGE_CY	MEDHHR_CY	MEDHINC_CY	MEDMAGE_CY	MEDNW_CY	MEDVAL_CY	MILLENN_CY	MINORITYCY	MLTFBASECY	MLTMBASECY	NEVMARR_CY	NHSPAI_CY	NHSPASN_CY	NHSPBLK_CY	NHSPMLT_CY	NHSPOTH_CY	NHSPPI_CY	NHSPWHT_CY	NOHS_CY	NONHISP_CY	OAGEBASECY	OLDRGENSCY	OTHFBASECY	OTHMBASECY	OTHRACE_CY	OWNER_CY	PACIFIC_CY	PAGEBASECY	PCI_CY	PIFBASE_CY	PIMBASE_CY	POP0_CY	POP15_CY	POP18UP_CY	POP20_CY	POP21UP_CY	POP25_CY	POP30_CY	POP35_CY	POP40_CY	POP45_CY	POP50_CY	POP55_CY	POP5_CY	POP60_CY	POP65_CY	POP70_CY	POP75_CY	POP80_CY	POP85_CY	POPDENS_CY	RACE2UP_CY	RACEBASECY	RENTER_CY	SMCOLL_CY	SOMEHS_CY	TADULT01	TADULT02	TADULT03	TADULT04	TADULT05	TADULT06	TADULT07	TADULT08	TADULT09	TADULT11	TADULT12	TADULT13	TADULT14	TADULT15	TADULT16	TADULT17	TADULT18	TADULT19	TADULT20	TADULT21	TADULT22	TADULT23	TADULT24	TADULT25	TADULT26	TADULT27	TADULT28	TADULT29	TADULT30	TADULT31	TADULT32	TADULT33	TADULT34	TADULT35	TADULT36	TADULT37	TADULT38	TADULT39	TADULT40	TADULT41	TADULT42	TADULT43	TADULT44	TADULT45	TADULT46	TADULT47	TADULT48	TADULT49	TADULT50	TADULT51	TADULT52	TADULT53	TADULT54	TADULT55	TADULT56	TADULT57	TADULT58	TADULT59	TADULT60	TADULT61	TADULT62	TADULT63	TADULT64	TADULT65	TADULT66	TADULT67	TADULT68	TADULTBASE	THH01	THH02	THH03	THH04	THH05	THH06	THH07	THH08	THH09	THH11	THH12	THH13	THH14	THH15	THH16	THH17	THH18	THH19	THH20	THH21	THH22	THH23	THH24	THH25	THH26	THH27	THH28	THH29	THH30	THH31	THH32	THH33	THH34	THH35	THH36	THH37	THH38	THH39	THH40	THH41	THH42	THH43	THH44	THH45	THH46	THH47	THH48	THH49	THH50	THH51	THH52	THH53	THH54	THH55	THH56	THH57	THH58	THH59	THH60	THH61	THH62	THH63	THH64	THH65	THH66	THH67	THH68	THHBASE	THHGRPL1	THHGRPL11	THHGRPL12	THHGRPL13	THHGRPL14	THHGRPL2	THHGRPL3	THHGRPL4	THHGRPL5	THHGRPL6	THHGRPL7	THHGRPL8	THHGRPL9	THHGRPU1	THHGRPU2	THHGRPU3	THHGRPU4	THHGRPU5	THHGRPU6	TOTHH_CY	TOTHU_CY	TOTPOP_CY	TSEGNUM	UNEMPRT_CY	UNEMP_CY	VACANT_CY	VAL0_CY	VAL150K_CY	VAL1M_CY	VAL1PT5MCY	VAL250K_CY	VAL2M_CY	VAL50K_CY	VAL750K_CY	VALBASE_CY	WAGEBASECY	WHITE_CY	WHTFBASECY	WHTMBASECY	WIDOWED_CY
0	0.085683	2.465018e-17	1.706127e-17	1.206350e-17	-2.418636e-17	-5.895328e-18	0.052559	0.053578	0.053072	0.021408	5.023896e-18	-3.009757e-18	0.078875	0.086244	3.655364e-18	0.039666	0.039064	-3.754861e-18	6.154518e-18	-2.263590e-18	3.418718e-20	3.215796e-18	0.074896	0.074896	0.074241	0.075580	2.357317e-18	1.196090e-18	-7.261180e-18	6.962755e-18	-1.683286e-18	4.051243e-18	-4.713840e-18	1.615958e-18	9.115194e-19	1.294890e-18	-5.218822e-18	-5.617389e-18	3.388000e-18	-5.696340e-18	-2.240383e-18	-1.960504e-18	9.940268e-19	9.715866e-19	3.287971e-18	-1.923931e-18	1.941226e-19	0.086474	0.085888	0.084687	-0.0	0.084626	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	0.084138	-0.0	0.085197	0.085924	0.086732	-0.0	-0.0	-0.0	0.072163	0.076025	0.078033	0.078956	0.079231	0.073096	-0.0	-0.0	0.085892	-0.0	-0.0	0.053072	0.021303	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	0.086432	0.085421	-0.0	-0.0	-0.0	-0.011538	-0.0	-0.008828	-0.011955	-0.0	-0.015808	0.002422	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	0.043703	-0.0	0.074476	-0.0	0.080397	0.059972	0.084554	-0.0	0.086238	0.079535	0.086322	0.079111	0.079858	0.079535	0.087039	0.062084	0.062084	-0.0	0.062408	0.061736	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	0.086152	0.085001	0.031455	-0.0	-0.0	-0.0	-0.0	-0.0	0.073387	0.079703	0.069097	0.074758	0.080037	0.067452	0.047312	0.050130	0.070482	0.073022	0.040283	0.080947	0.078418	0.057543	0.079927	0.075710	0.055159	0.064269	0.073370	0.069975	0.056940	0.046094	0.014159	0.059571	0.047669	0.054627	0.059226	0.075081	0.066858	0.045659	0.056298	0.042672	0.074924	0.072042	0.051459	0.065963	0.076449	0.069033	0.066245	0.073411	0.069773	0.065770	0.076703	0.077049	0.049963	0.054998	0.031138	0.059298	0.053029	0.044205	0.072131	0.076982	0.071014	0.061065	0.063811	0.054572	0.063491	0.053655	0.052528	0.044027	0.072151	0.060901	0.050955	0.033574	0.075765	0.074839	0.062812	-0.0	0.073953	0.079825	0.069087	0.074957	0.080305	0.068421	0.047543	0.050100	0.070942	0.072983	0.040673	0.080895	0.078635	0.056886	0.079575	0.075715	0.055016	0.063674	0.072955	0.069331	0.056168	0.045753	0.013229	0.059014	0.046911	0.053891	0.059649	0.075547	0.066238	0.045997	0.056547	0.042827	0.075695	0.072284	0.051457	0.066301	0.076254	0.068873	0.066576	0.073935	0.069686	0.065778	0.076579	0.076035	0.049554	0.054197	0.030470	0.059348	0.051329	0.044494	0.071894	0.077392	0.070773	0.061195	0.063641	0.054345	0.062992	0.053647	0.052797	0.044400	0.072230	0.060857	0.050516	0.030823	0.073175	0.070365	0.060398	-0.0	-0.0	0.082254	0.064420	0.071520	0.075812	0.065361	0.070086	0.078674	0.078209	0.062453	0.072502	0.084328	0.078787	0.070914	-0.0	0.083569	0.084980	0.070164	0.063236	-0.0	-0.0	-0.0	-0.001014	0.023665	-0.0	-0.0	0.070085	0.079827	0.074140	0.068529	0.085044	0.068394	0.067000	0.072713	0.087039	0.085779	0.085779	0.085664	0.085881	0.085940
1	0.020401	-4.515305e-17	4.921657e-17	-5.089035e-17	1.364893e-17	-2.587692e-17	-0.034574	-0.034343	-0.034468	-0.082945	-2.002968e-17	8.590029e-18	0.065508	-0.014635	-1.808775e-19	0.071206	0.045470	2.557926e-18	-1.558028e-17	-1.449645e-17	-3.559930e-18	-2.388000e-18	-0.004013	-0.004013	-0.003847	-0.004269	-1.557907e-18	-4.505577e-18	3.060224e-18	-1.475331e-17	-7.810819e-18	-4.422439e-18	-2.418346e-18	-5.500972e-18	-9.763423e-18	-6.925259e-18	-1.613203e-18	-3.095488e-18	-3.172126e-18	-7.212717e-18	-2.898549e-18	-2.690478e-18	1.752402e-18	2.462416e-18	2.815774e-19	3.954307e-19	4.885119e-19	-0.006136	-0.005213	0.001470	0.0	-0.033630	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.026700	0.0	-0.032828	-0.031828	-0.027269	0.0	0.0	0.0	0.029376	0.068137	0.058924	0.058497	0.040152	0.053106	0.0	0.0	-0.013840	0.0	0.0	-0.034468	-0.082883	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.005139	0.003537	0.0	0.0	0.0	0.004787	0.0	0.004835	0.001448	0.0	0.005028	0.090090	0.0	0.0	0.0	0.0	0.0	0.0	-0.057929	0.0	-0.006618	0.0	0.056056	0.018270	-0.023252	0.0	-0.010651	0.041177	-0.004183	0.044295	0.038425	0.041177	-0.014953	0.027291	0.027291	0.0	0.028773	0.025898	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.005171	0.002221	0.096918	0.0	0.0	0.0	0.0	0.0	0.077742	0.019375	-0.020860	0.051739	0.040435	0.099562	0.134863	0.096832	0.056042	0.053330	0.134557	-0.019198	0.015402	-0.115544	-0.028065	-0.034211	0.087488	-0.099554	-0.071594	-0.053730	-0.097189	0.029909	-0.108946	-0.042895	-0.131720	-0.078684	0.092232	0.014548	-0.048333	0.030158	-0.000352	0.137934	-0.002385	-0.026550	0.062636	0.067654	-0.062313	-0.095890	0.041771	0.080823	0.037290	0.001283	0.012376	0.044967	-0.119622	-0.102339	-0.099934	-0.086832	-0.094661	0.089435	-0.037392	0.004218	-0.041333	-0.035743	-0.026033	-0.123738	-0.110088	-0.064709	0.130808	0.073489	0.036109	0.083327	0.087660	0.033120	-0.070204	-0.023338	0.052308	0.0	0.075668	0.017482	-0.023278	0.049717	0.037493	0.096809	0.134909	0.096946	0.055380	0.051508	0.134445	-0.023970	0.012813	-0.117875	-0.030395	-0.038521	0.085433	-0.100777	-0.073276	-0.056165	-0.098673	0.029621	-0.109891	-0.043018	-0.132243	-0.081836	0.091990	0.011743	-0.050315	0.029866	0.000030	0.138497	-0.005137	-0.029566	0.063132	0.064886	-0.063504	-0.095880	0.041540	0.078440	0.038162	0.002199	0.011252	0.047321	-0.120193	-0.103700	-0.100297	-0.088392	-0.097204	0.088955	-0.041181	0.002230	-0.043942	-0.036755	-0.027361	-0.124069	-0.111245	-0.065343	0.130624	0.073384	0.033686	0.083089	0.092768	0.034786	-0.078677	-0.047789	0.025388	0.0	0.0	-0.002094	-0.096182	0.090065	-0.062050	0.107495	0.077462	-0.050829	-0.025066	-0.087196	-0.028302	0.016160	0.046559	0.090392	0.0	-0.039740	0.039568	-0.107237	-0.114634	0.0	0.0	0.0	-0.123445	0.017914	0.0	0.0	-0.105495	-0.061881	0.079118	0.094034	0.005818	0.094706	-0.110719	0.091677	-0.014961	-0.019127	-0.019127	-0.018982	-0.019286	-0.011852
2	-0.036948	1.688129e-17	-8.885888e-17	-6.544145e-17	7.119450e-17	1.401133e-17	0.138857	0.138144	0.138520	0.169852	-2.357290e-17	-1.765870e-17	0.005683	-0.015150	1.381044e-18	0.147914	0.138066	-1.786496e-18	-7.245192e-18	3.711866e-18	4.888289e-18	1.631003e-18	-0.057173	-0.057173	-0.059538	-0.054682	-7.193773e-18	-4.058091e-18	-1.184503e-17	6.308261e-18	-1.468394e-17	7.385927e-18	-1.569572e-17	4.501841e-18	4.314889e-18	-1.053572e-18	9.609118e-19	-7.415317e-19	6.003387e-18	1.111243e-18	2.451795e-17	1.268278e-17	-5.597962e-18	2.461913e-18	-9.544324e-18	4.485265e-18	-2.678288e-19	-0.039549	-0.046468	-0.056877	0.0	-0.024692	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.038479	0.0	-0.032883	-0.028070	-0.021975	0.0	0.0	0.0	0.100647	0.057376	-0.030139	0.055825	0.053661	0.071092	0.0	0.0	-0.041263	0.0	0.0	0.138520	0.170232	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.037397	-0.044245	0.0	0.0	0.0	-0.181368	0.0	-0.184369	-0.153420	0.0	-0.175238	-0.024327	0.0	0.0	0.0	0.0	0.0	0.0	0.142495	0.0	-0.057626	0.0	-0.024296	0.128635	-0.042146	0.0	-0.035081	0.050818	-0.041496	0.050484	0.051163	0.050818	-0.027854	0.123618	0.123618	0.0	0.121862	0.125225	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	-0.042634	-0.052407	-0.143522	0.0	0.0	0.0	0.0	0.0	-0.024503	0.003177	0.103819	-0.031631	-0.006955	0.001720	-0.027489	-0.017360	0.013017	-0.022303	0.003014	0.035865	0.061208	0.043367	-0.060645	-0.012981	-0.063873	-0.037757	-0.068868	-0.067396	-0.107239	0.026627	0.055607	0.006354	-0.075435	0.140275	0.046866	0.092063	0.089711	0.082724	0.111225	0.000296	0.012145	0.089855	-0.034275	-0.013408	0.007987	-0.025098	0.005127	-0.069318	-0.006447	0.032874	-0.061916	-0.054699	-0.002003	-0.037959	0.070259	0.082087	-0.023162	-0.091292	0.078742	0.058526	-0.004982	-0.087630	-0.078602	-0.026438	-0.058690	-0.088308	0.004454	0.067604	0.052954	-0.044996	-0.022442	0.112413	-0.018657	-0.060983	0.025741	0.0	-0.024553	0.001385	0.102382	-0.033776	-0.008540	0.002124	-0.028443	-0.017153	0.011107	-0.020837	0.002355	0.032978	0.059259	0.040632	-0.062508	-0.014503	-0.066097	-0.041458	-0.069693	-0.069705	-0.108260	0.026381	0.054180	0.005412	-0.076570	0.138952	0.046258	0.090687	0.090814	0.082591	0.111279	-0.001429	0.011623	0.086060	-0.034118	-0.016984	0.005907	-0.028557	0.004776	-0.068891	-0.006276	0.032151	-0.062059	-0.056985	-0.004664	-0.040064	0.068663	0.079716	-0.022534	-0.091949	0.076612	0.056585	-0.002580	-0.089692	-0.080588	-0.029529	-0.061226	-0.089821	0.003343	0.066910	0.055397	-0.045505	-0.018725	0.119030	-0.012483	-0.041485	-0.021118	0.0	0.0	0.000061	-0.082621	-0.007645	0.005437	-0.018426	-0.023834	0.045090	-0.069725	-0.064967	0.120359	-0.000135	-0.037833	-0.014705	0.0	-0.023279	-0.010126	0.005014	-0.052907	0.0	0.0	0.0	0.041781	0.009035	0.0	0.0	-0.021272	-0.040786	-0.018901	-0.006572	-0.017319	-0.019541	-0.049012	-0.001518	-0.027858	-0.027286	-0.027286	-0.029240	-0.025280	-0.042054
3	0.034192	-3.086556e-17	-1.398148e-17	-2.235588e-17	1.655388e-17	-1.331722e-16	-0.040741	-0.042075	-0.041402	-0.079849	4.623954e-17	-1.805611e-17	0.011894	-0.032501	2.771436e-17	0.060366	0.000587	-1.593049e-17	2.424793e-17	3.309880e-17	3.082814e-17	-2.268440e-17	0.138184	0.138184	0.140570	0.135368	1.546415e-18	-1.437585e-17	-2.140256e-18	1.256237e-17	-1.703816e-17	4.748225e-18	-9.666831e-18	-9.665212e-19	-1.956447e-17	-1.827287e-17	-5.064333e-18	-9.547341e-18	-9.287188e-18	-2.172381e-17	7.173587e-18	1.063483e-18	3.963385e-18	3.658758e-18	1.258421e-18	-1.892552e-18	-1.944177e-20	0.007884	0.004274	-0.007940	-0.0	0.019728	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	0.042990	-0.0	0.018131	0.011598	0.004224	-0.0	-0.0	-0.0	-0.014306	0.015581	0.071734	0.015658	0.037206	0.049469	-0.0	-0.0	0.010438	-0.0	-0.0	-0.041402	-0.079431	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.004309	-0.015553	-0.0	-0.0	-0.0	-0.126203	-0.0	-0.119779	-0.121182	-0.0	-0.134107	-0.175189	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.049415	-0.0	0.139727	-0.0	0.029609	-0.001452	-0.026230	-0.0	0.011992	0.036314	-0.000074	0.032367	0.039500	0.036314	-0.000836	0.005772	0.005772	-0.0	0.006450	0.005076	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	0.000724	-0.010546	0.118093	-0.0	-0.0	-0.0	-0.0	-0.0	0.063594	-0.012051	0.019572	-0.067867	-0.047615	0.006432	-0.035581	-0.008530	0.003469	0.020018	-0.007049	-0.021222	-0.035259	0.000708	-0.022600	-0.101570	-0.118776	-0.055221	-0.031659	-0.089724	-0.025832	-0.161236	-0.117220	-0.142956	-0.046711	0.024060	-0.023413	0.028066	0.031141	-0.072017	0.057011	0.019739	-0.083357	-0.022868	-0.016470	-0.107639	-0.087287	0.038265	-0.013059	-0.002777	0.004962	-0.041452	-0.062237	0.014589	0.063053	0.039530	0.087161	0.015202	0.105475	0.108501	0.034434	-0.004215	-0.090672	0.158822	0.155846	-0.017967	-0.004179	0.179098	-0.030049	-0.060296	0.004310	-0.082852	0.051327	0.138182	-0.041152	-0.020716	0.104402	-0.0	0.063293	-0.011568	0.020512	-0.068028	-0.046495	0.009387	-0.036065	-0.008305	0.003409	0.020995	-0.006684	-0.019518	-0.035183	0.001436	-0.021723	-0.097348	-0.120100	-0.055351	-0.032459	-0.089125	-0.025949	-0.163205	-0.117852	-0.144925	-0.047722	0.024957	-0.023598	0.028793	0.031323	-0.072444	0.056156	0.018122	-0.078593	-0.021375	-0.016501	-0.107952	-0.086560	0.037996	-0.012322	-0.002841	0.003315	-0.044061	-0.064218	0.007895	0.063623	0.040998	0.086370	0.014759	0.107342	0.108748	0.035220	-0.001217	-0.101133	0.159759	0.156709	-0.018484	-0.006757	0.178954	-0.029026	-0.060478	0.004285	-0.083640	0.051736	0.138007	-0.036688	-0.026776	0.127430	-0.0	-0.0	0.050636	0.075515	0.004112	-0.023481	0.008620	0.034013	-0.013003	-0.071649	-0.114943	0.011646	-0.051948	-0.013036	0.039021	-0.0	-0.042561	-0.011836	-0.019821	-0.008228	-0.0	-0.0	-0.0	0.063428	0.151416	-0.0	-0.0	0.025468	-0.008786	0.026361	0.016305	-0.042277	0.002868	0.031481	0.013469	-0.000843	-0.025336	-0.025336	-0.025044	-0.025618	0.016091
4	0.004716	-1.430180e-16	-5.434954e-17	-8.392303e-17	7.696711e-17	-5.979270e-17	0.002666	0.000115	0.001389	0.021637	-5.969440e-17	-1.000038e-16	0.042541	0.006503	2.588828e-17	0.037708	-0.023944	1.826221e-17	-5.516496e-17	-3.158504e-17	-7.126614e-18	2.379529e-17	-0.001835	-0.001835	-0.001079	-0.002547	1.012482e-17	-3.062674e-17	-3.047211e-17	3.380177e-17	-4.464840e-18	6.540691e-18	-5.002371e-17	5.190371e-18	-1.877184e-17	-9.274039e-19	-5.063075e-19	-1.451145e-17	-2.960811e-18	-2.194715e-17	1.746023e-17	8.405538e-18	-1.148994e-18	4.534027e-18	-8.346499e-18	2.591063e-19	-1.549356e-18	-0.013421	-0.003895	0.014448	-0.0	-0.037604	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.024591	-0.0	-0.020505	-0.012579	-0.003640	-0.0	-0.0	-0.0	0.052477	-0.000804	-0.004223	0.019977	0.030445	-0.026840	-0.0	-0.0	-0.007608	-0.0	-0.0	0.001389	0.021917	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.009209	0.006807	-0.0	-0.0	-0.0	-0.180702	-0.0	-0.183090	-0.206424	-0.0	-0.175900	0.111883	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.015232	-0.0	-0.000609	-0.0	0.001969	-0.014887	0.028886	-0.0	0.015681	0.028646	-0.007321	0.030711	0.027023	0.028646	0.004105	-0.017183	-0.017183	-0.0	-0.014958	-0.019338	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.0	-0.006184	0.011599	0.003413	-0.0	-0.0	-0.0	-0.0	-0.0	0.026977	0.073637	0.067760	0.058773	-0.067721	-0.007285	-0.021224	-0.004469	0.062308	0.087876	-0.016289	0.036441	-0.006380	-0.031238	0.024200	0.015971	0.008675	0.084179	-0.013023	0.081733	0.048306	-0.214261	0.222455	-0.101440	0.088093	-0.003817	-0.008353	-0.038698	0.038793	-0.060747	-0.099683	-0.015027	-0.017912	0.017568	0.028049	-0.046073	-0.012666	0.050067	-0.173576	-0.001848	-0.054285	-0.208127	0.014619	0.007151	-0.148172	-0.143806	-0.076754	-0.110897	-0.137987	0.027116	0.039536	-0.029703	-0.004117	0.040295	0.027499	0.092501	-0.061497	0.014700	0.000639	0.025643	0.022872	0.030146	0.022074	-0.054885	0.053665	0.080355	0.003059	-0.0	0.026535	0.074670	0.069085	0.059135	-0.069760	-0.008299	-0.019740	-0.004229	0.062245	0.087860	-0.016967	0.040069	-0.004271	-0.027570	0.024083	0.016790	0.011181	0.087332	-0.010615	0.083171	0.050282	-0.212491	0.221414	-0.099466	0.091350	0.000063	-0.008181	-0.037953	0.038919	-0.061458	-0.098551	-0.013801	-0.016816	0.020193	0.028182	-0.044125	-0.008362	0.049741	-0.171934	-0.001856	-0.053998	-0.206596	0.015718	0.011897	-0.147152	-0.143143	-0.075278	-0.108774	-0.139947	0.029311	0.040851	-0.028016	-0.006270	0.041770	0.029302	0.094845	-0.056585	0.015553	0.000138	0.025403	0.022012	0.029312	0.014971	-0.050310	0.047915	0.067537	-0.057710	-0.0	-0.0	0.029579	0.026103	0.031212	0.050535	0.018911	0.056165	-0.003675	0.041201	0.084287	-0.005597	0.021186	-0.056053	0.037368	-0.0	0.039777	0.016025	-0.039613	-0.041406	-0.0	-0.0	-0.0	-0.165740	-0.129932	-0.0	-0.0	-0.032208	0.009972	-0.018506	-0.006533	0.000783	-0.028449	0.007487	-0.014164	0.004104	0.017963	0.017963	0.017518	0.018443	-0.019350